{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This notebook demonstrates the use of a reweighing pre-processing algorithm for bias mitigation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "# Load all necessary packages\n",
    "import sys\n",
    "sys.path.append(\"../\")\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "\n",
    "from aif360.datasets import BinaryLabelDataset\n",
    "from aif360.datasets import AdultDataset, GermanDataset, CompasDataset\n",
    "from aif360.metrics import BinaryLabelDatasetMetric\n",
    "from aif360.metrics import ClassificationMetric\n",
    "from aif360.algorithms.preprocessing.reweighing import Reweighing\n",
    "from aif360.algorithms.preprocessing.optim_preproc_helpers.data_preproc_functions\\\n",
    "        import load_preproc_data_adult, load_preproc_data_german, load_preproc_data_compas\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "from IPython.display import Markdown, display\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from common_utils import compute_metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load dataset and set options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## import dataset\n",
    "dataset_used = \"adult\" # \"adult\", \"german\", \"compas\"\n",
    "protected_attribute_used = 1 # 1, 2\n",
    "\n",
    "\n",
    "if dataset_used == \"adult\":\n",
    "#     dataset_orig = AdultDataset()\n",
    "    if protected_attribute_used == 1:\n",
    "        privileged_groups = [{'sex': 1}]\n",
    "        unprivileged_groups = [{'sex': 0}]\n",
    "        dataset_orig = load_preproc_data_adult(['sex'])\n",
    "    else:\n",
    "        privileged_groups = [{'race': 1}]\n",
    "        unprivileged_groups = [{'race': 0}]\n",
    "        dataset_orig = load_preproc_data_adult(['race'])\n",
    "    \n",
    "elif dataset_used == \"german\":\n",
    "#     dataset_orig = GermanDataset()\n",
    "    if protected_attribute_used == 1:\n",
    "        privileged_groups = [{'sex': 1}]\n",
    "        unprivileged_groups = [{'sex': 0}]\n",
    "        dataset_orig = load_preproc_data_german(['sex'])\n",
    "    else:\n",
    "        privileged_groups = [{'age': 1}]\n",
    "        unprivileged_groups = [{'age': 0}]\n",
    "        dataset_orig = load_preproc_data_german(['age'])\n",
    "    \n",
    "elif dataset_used == \"compas\":\n",
    "#     dataset_orig = CompasDataset()\n",
    "    if protected_attribute_used == 1:\n",
    "        privileged_groups = [{'sex': 1}]\n",
    "        unprivileged_groups = [{'sex': 0}]\n",
    "        dataset_orig = load_preproc_data_compas(['sex'])\n",
    "    else:\n",
    "        privileged_groups = [{'race': 1}]\n",
    "        unprivileged_groups = [{'race': 0}]\n",
    "        dataset_orig = load_preproc_data_compas(['race'])\n",
    "\n",
    "all_metrics =  [\"Statistical parity difference\",\n",
    "                   \"Average odds difference\",\n",
    "                   \"Equal opportunity difference\"]\n",
    "\n",
    "#random seed for calibrated equal odds prediction\n",
    "np.random.seed(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Split into train, and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the dataset and split into train and test\n",
    "dataset_orig_train, dataset_orig_vt = dataset_orig.split([0.7], shuffle=True)\n",
    "dataset_orig_valid, dataset_orig_test = dataset_orig_vt.split([0.5], shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Clean up training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "#### Training Dataset shape"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(34189, 18)\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "#### Favorable and unfavorable labels"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1.0, 0.0)\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "#### Protected attribute names"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['sex']\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "#### Privileged and unprivileged protected attribute values"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "([array([1.])], [array([0.])])\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "#### Dataset feature names"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['race', 'sex', 'Age (decade)=10', 'Age (decade)=20', 'Age (decade)=30', 'Age (decade)=40', 'Age (decade)=50', 'Age (decade)=60', 'Age (decade)=>=70', 'Education Years=6', 'Education Years=7', 'Education Years=8', 'Education Years=9', 'Education Years=10', 'Education Years=11', 'Education Years=12', 'Education Years=<6', 'Education Years=>12']\n"
     ]
    }
   ],
   "source": [
    "# print out some labels, names, etc.\n",
    "display(Markdown(\"#### Training Dataset shape\"))\n",
    "print(dataset_orig_train.features.shape)\n",
    "display(Markdown(\"#### Favorable and unfavorable labels\"))\n",
    "print(dataset_orig_train.favorable_label, dataset_orig_train.unfavorable_label)\n",
    "display(Markdown(\"#### Protected attribute names\"))\n",
    "print(dataset_orig_train.protected_attribute_names)\n",
    "display(Markdown(\"#### Privileged and unprivileged protected attribute values\"))\n",
    "print(dataset_orig_train.privileged_protected_attributes, \n",
    "      dataset_orig_train.unprivileged_protected_attributes)\n",
    "display(Markdown(\"#### Dataset feature names\"))\n",
    "print(dataset_orig_train.feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Metric for original training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "#### Original training dataset"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference in mean outcomes between unprivileged and privileged groups = -0.190698\n"
     ]
    }
   ],
   "source": [
    "# Metric for the original dataset\n",
    "metric_orig_train = BinaryLabelDatasetMetric(dataset_orig_train, \n",
    "                                             unprivileged_groups=unprivileged_groups,\n",
    "                                             privileged_groups=privileged_groups)\n",
    "display(Markdown(\"#### Original training dataset\"))\n",
    "print(\"Difference in mean outcomes between unprivileged and privileged groups = %f\" % metric_orig_train.mean_difference())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train with and transform the original training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "RW = Reweighing(unprivileged_groups=unprivileged_groups,\n",
    "               privileged_groups=privileged_groups)\n",
    "RW.fit(dataset_orig_train)\n",
    "dataset_transf_train = RW.transform(dataset_orig_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Testing \n",
    "assert np.abs(dataset_transf_train.instance_weights.sum()-dataset_orig_train.instance_weights.sum())<1e-6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Metric with the transformed training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "#### Transformed training dataset"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference in mean outcomes between unprivileged and privileged groups = -0.000000\n"
     ]
    }
   ],
   "source": [
    "metric_transf_train = BinaryLabelDatasetMetric(dataset_transf_train, \n",
    "                                         unprivileged_groups=unprivileged_groups,\n",
    "                                         privileged_groups=privileged_groups)\n",
    "display(Markdown(\"#### Transformed training dataset\"))\n",
    "print(\"Difference in mean outcomes between unprivileged and privileged groups = %f\" % metric_transf_train.mean_difference())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Testing \n",
    "assert np.abs(metric_transf_train.mean_difference()) < 1e-6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train classifier on original data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Logistic regression classifier and predictions\n",
    "scale_orig = StandardScaler()\n",
    "X_train = scale_orig.fit_transform(dataset_orig_train.features)\n",
    "y_train = dataset_orig_train.labels.ravel()\n",
    "w_train = dataset_orig_train.instance_weights.ravel()\n",
    "\n",
    "lmod = LogisticRegression()\n",
    "lmod.fit(X_train, y_train, \n",
    "         sample_weight=dataset_orig_train.instance_weights)\n",
    "y_train_pred = lmod.predict(X_train)\n",
    "\n",
    "# positive class index\n",
    "pos_ind = np.where(lmod.classes_ == dataset_orig_train.favorable_label)[0][0]\n",
    "\n",
    "dataset_orig_train_pred = dataset_orig_train.copy()\n",
    "dataset_orig_train_pred.labels = y_train_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Obtain scores for original validation and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_orig_valid_pred = dataset_orig_valid.copy(deepcopy=True)\n",
    "X_valid = scale_orig.transform(dataset_orig_valid_pred.features)\n",
    "y_valid = dataset_orig_valid_pred.labels\n",
    "dataset_orig_valid_pred.scores = lmod.predict_proba(X_valid)[:,pos_ind].reshape(-1,1)\n",
    "\n",
    "dataset_orig_test_pred = dataset_orig_test.copy(deepcopy=True)\n",
    "X_test = scale_orig.transform(dataset_orig_test_pred.features)\n",
    "y_test = dataset_orig_test_pred.labels\n",
    "dataset_orig_test_pred.scores = lmod.predict_proba(X_test)[:,pos_ind].reshape(-1,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find the optimal classification threshold from the validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best balanced accuracy (no reweighing) = 0.7473\n",
      "Optimal classification threshold (no reweighing) = 0.2674\n"
     ]
    }
   ],
   "source": [
    "num_thresh = 100\n",
    "ba_arr = np.zeros(num_thresh)\n",
    "class_thresh_arr = np.linspace(0.01, 0.99, num_thresh)\n",
    "for idx, class_thresh in enumerate(class_thresh_arr):\n",
    "    \n",
    "    fav_inds = dataset_orig_valid_pred.scores > class_thresh\n",
    "    dataset_orig_valid_pred.labels[fav_inds] = dataset_orig_valid_pred.favorable_label\n",
    "    dataset_orig_valid_pred.labels[~fav_inds] = dataset_orig_valid_pred.unfavorable_label\n",
    "    \n",
    "    classified_metric_orig_valid = ClassificationMetric(dataset_orig_valid,\n",
    "                                             dataset_orig_valid_pred, \n",
    "                                             unprivileged_groups=unprivileged_groups,\n",
    "                                             privileged_groups=privileged_groups)\n",
    "    \n",
    "    ba_arr[idx] = 0.5*(classified_metric_orig_valid.true_positive_rate()\\\n",
    "                       +classified_metric_orig_valid.true_negative_rate())\n",
    "\n",
    "best_ind = np.where(ba_arr == np.max(ba_arr))[0][0]\n",
    "best_class_thresh = class_thresh_arr[best_ind]\n",
    "\n",
    "print(\"Best balanced accuracy (no reweighing) = %.4f\" % np.max(ba_arr))\n",
    "print(\"Optimal classification threshold (no reweighing) = %.4f\" % best_class_thresh)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predictions from the original test set at the optimal classification threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "#### Predictions from original testing data"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 14%|█▍        | 14/100 [00:00<00:00, 134.43it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification threshold used = 0.2674\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 41%|████      | 41/100 [00:00<00:00, 131.04it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Balanced accuracy = 0.7417\n",
      "Statistical parity difference = -0.3576\n",
      "Disparate impact = 0.2774\n",
      "Average odds difference = -0.3281\n",
      "Equal opportunity difference = -0.4001\n",
      "Theil index = 0.1128\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 67%|██████▋   | 67/100 [00:00<00:00, 127.49it/s]../aif360/metrics/dataset_metric.py:94: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  return metric_fun(privileged=False) / metric_fun(privileged=True)\n",
      "100%|██████████| 100/100 [00:00<00:00, 121.44it/s]\n"
     ]
    }
   ],
   "source": [
    "display(Markdown(\"#### Predictions from original testing data\"))\n",
    "bal_acc_arr_orig = []\n",
    "disp_imp_arr_orig = []\n",
    "avg_odds_diff_arr_orig = []\n",
    "\n",
    "print(\"Classification threshold used = %.4f\" % best_class_thresh)\n",
    "for thresh in tqdm(class_thresh_arr):\n",
    "    \n",
    "    if thresh == best_class_thresh:\n",
    "        disp = True\n",
    "    else:\n",
    "        disp = False\n",
    "    \n",
    "    fav_inds = dataset_orig_test_pred.scores > thresh\n",
    "    dataset_orig_test_pred.labels[fav_inds] = dataset_orig_test_pred.favorable_label\n",
    "    dataset_orig_test_pred.labels[~fav_inds] = dataset_orig_test_pred.unfavorable_label\n",
    "    \n",
    "    metric_test_bef = compute_metrics(dataset_orig_test, dataset_orig_test_pred, \n",
    "                                      unprivileged_groups, privileged_groups,\n",
    "                                      disp = disp)\n",
    "\n",
    "    bal_acc_arr_orig.append(metric_test_bef[\"Balanced accuracy\"])\n",
    "    avg_odds_diff_arr_orig.append(metric_test_bef[\"Average odds difference\"])\n",
    "    disp_imp_arr_orig.append(metric_test_bef[\"Disparate impact\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Display results for all thresholds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1Y3cnZ+F86SkVQoigI8P3wmd2H8rllg9W0SYxhleu7ENoXSlhVAMvv/wyAKNGjap1W3GRYfRp3aBuzCt1Fs6XnlIhhAg6kpQKnzhWUMz4936nVMPb1/anflS43SHZasGCBR5tb3CHJF76diuXzPj5pGshShGiTF3TUKUIDwth7IDWnNvdM2WpPEp6SoUQImhJUiq8rrRUc/e8tWw7eIx3rzudtkmxdodku/h4z5ZvGtWnJev3ZVFQXHrSNa2hVGtKtaakVLMvI4cJ769iTP9WPHRBN2Ij/eiPAekpFUKIoOVHfxuJuurFJVv45s90HhnZjTM7emZxT6CbN28eAKNHj/ZIe60TY/jXtf0t3VtYXMoLS7bwxg/bWZFyiBdG96Zv64YeiaPW0tMhIsJsMyqEECKoyEIn4VVfrkvl5e+2cUW/lowb1NbucPzG66+/zuuvv27LuyPCQrhveBfmjj+D4hLN5W+s4IVvtlBccnIvq885txhVwTvfWAghgpXSWtsdg61iY2N1Tk6O3WHUSev3ZXHZG8vp3jyeOeMHEBkW4DU0PSg3NxeAmJgYW+M4ml/Eows28NmafZzeLoHXruxLo3qR9gV03nmmLNTKlfbFIIQQfkwplau1rpPz4KSnVHjFgex8Jrz3Ow1jInhj7GmSkLqIiYmxPSEFqB8VzvOje/Pi6N6s25vJBa/8xOrdNtY7PXBAFjkJIUSQkqRUeNSew7k8/sWfnP3sUg7lFPLW1f3s7XnzUx988AEffPCB3WEcd3GfFnx2y2AiwkIY8+YvzPl1tz2ByBajQggRtGT4XobvPeJ/ezKZ+VMKi9bvRwEXnNqMm4ecQpemsmClIkOGDAFg6dKltsbhKjO3kDvnruXHLQcZ078Vj17Y3Xdbl2oNkZEwaRJMm+abdwohRICpy8P3svpe1NrzX2/m5e+2US8yjBvPbMe1g9rSvEG03WH5tW+++cbuECrUICaCd8b154VvtvDq99tQSvHMqJ6+eXlmJhQVSU+pEEIEKUlKRa19tmYfA9sn8tY1p1EvyIviWxUe7r//nEJDFPf+rTOpWXl8uS6Vxy7sTkSYD2b6SOF8IYQIajKnVNTKgex89h7JY2jXxpKQumH27NnMnj3b7jCqNKJHM7Lzi1mRcsg3L5TC+UIIEdQkKRW1snpXJgB9/KX4eoAIhKT0zI5JxEaEsnh9mm9e6OwplaRUCCGCkiSlolbW7D5CRGgIPVrIgiZ3LF261O8WObmKCg/l7C6N+XpDOiWlPlgQ6ewpleF7IYQISpKUilpZtesI3VvUlzqkddTwHk05lFPI7zsPe/9l6elmJ6ck2YpWCCGCkSSlosYKi0tZty/Lf/ZNDyAzZ85k5syZdodRrbM7NyYiLIRF6/d7/2UHDpiENFR+wRFCiGAkSamosT/TjlJYXCpJaQ3MmzePefPm2R1GtWIjw/hLx0Z8tWE/Xq9pLIXzhRAiqElJKFFjq3eZ7Sj7tmlgcySBZ8mSJXaHYNnwHk1ZsjGddXuz6NXKi/+u09NlPqkQQgQx6SkVNbZ69xGax0fRLF4K5ddl53RtTFiI8v4Q/oED0lMqhBBBTJJSUWNrdmfSp40M3dfEjBkzmDFjht1hWNIgJoKBpySyeH2ad4fwpadUCCGCmiSlokbSj+azLzNP5pPW0BdffMEXX3xhdxiW/a17U3YeymVL+jHvvCA3F44dk55SIYQIYpKUiho5Pp+0tcwnrYlFixaxaNEiu8Ow7NzuTVAKFnmrkL7s5iSEEEFPklJRI6t3HyEiLITuzePtDkX4QON6UfRr05DF3ppXKoXzhRAi6ElSKmpk1a4j9GwRT0SY/CdUEy+99BIvvfSS3WG45W/dm7JpfzY7M3I837hsMSqEEEFPSkIJtxUUl7B+31GuHdTG7lAC1rfffgvAXXfdZXMk1g3v0ZQn/7uRj1ft4dK+Lctdyy0sIS0rn7SsPFIz89mflUdRiaZvm4ac0T6Brk3rExKiKm9cekqFECLoSVIq3LYh9SiFJVI0vzY+//zzmj9cXAxHjkCjRp4LyIKWDWM4tWU8r32/nde+317pfeGhiib1owD47x9mDmp8dDint0ugT+sGJ21JGxGquGjXXuqDJKVCCBHEJCkVbjtRNF+SUlu8+Sbcdx/s2OHzxPTVv/dlzZ4jJ52PDAulWXwUzRpEkRQbebxXNDUzj193HOKX7Yf5ZcchvvkzvcJ2i79dw5ioWP7Yn0f/tlEoVUWvqhBCiDpJklLhtjW7M2nRIPp4b5hw33PPPQfAvffe6/7Da9dCTg589BHcdpuHI6ta68QYWifGWL6/eYNoLunTkkv6mOH+YwXFlJSWr3V66FgBeStmcCAmniveXEHXZvUZN6gNl5/WquohfyGEEHWKLatUlFK3KqV2KKXylVKrlFJnVXHvbKWUruCT43JfsqOtfKVUilLqZu//JMFp9e4j0ktaSytWrGDFihU1ezglxRw//NBzAflIXGQY8dHh5T7tG8XRPTSPlp3bMG1UT7TW3PfpH8z5bbfd4QohhPAhnyelSqnRwEvA00AfYDmwSCnVupJH7gKauXxSgI/KtNkOWOhoqw/wDPCKUupSL/0YQSstK4+0rHypT1pLn376KZ9++mnNHt6+HcLCYMWKEwlqTWgNe/fCp5/CQw/BJ5+Y+ap2OHCA0KZNGXN6axbddRbtkmL5dmPFQ/1CCCHqJjt6SicBs7XWM7XWG7XWdwBpwC0V3ay1ztJa73d+gFOA9sDMMrfdDKRqre9wtDkTeBeowdioqMrqXZkAssjJLoWFsGcPjB1rvne3t1RrMyf1kkugRQto1QouuwyefBIuvxw6dIAXXoCjRz0fe1XS04+Xg1JKkdypEStSDpFfVOLbOIQQQtjGp0mpUioCOA342uXS18Agi82MBzZorZeXOTewgja/AvoppcJrEquo2OrdR4gMC6Frs/p2hxLQpk2bxrRp09x/cNcuKC2FIUMgOdkkpe7sRz9/Ptx8M/zxBwwdCi+/DL/+auaozp8PbdrApEnQsiXcfTfs91Kx/LKKi+HQoXIr75M7NSK/qJSVOw97//1CCCH8gq8XOiUBoYDruFw6cE51Dyul4oErgAdcLjUFllTQZpjjneX2RlRKTQAmAERERFgMXWxMO8ri9fs5taUUza+ttWvX1uxB53B9+/Zw1VUwYQKsWgX9+lX/bHExPPggdO1qktLQ8qWZuPhi81m1yvSWvvqqSVh//hm8uRo+I8Mk1mUK5w9on0BEWAg/bD7IWR19W2FACCGEPQItsxiLifn92jSitX5La91Pa90vLEwKEFSnuKSUGUu3ceGryygoLuHeczvbHVLAmzt3LnPnznX/QWdSesopZtg9IsL6EP5778GmTfDUUycnpGWddhp88AG89pqZt7pwoftxuqOCwvkxEWGc3jaBH7ce9O67hRBC+A1fJ6UZQAngupdgE8DKOOF44FOtteuY3v5K2ix2vFPUUMrBY1z+5gr+uXgzw7o14eu7kxnQPtHusILX9u0QFQVNm0LDhnD++TB3bvULlPLz4ZFH4PTTTW+oFdddZ5LfqVPNlAFvqWSL0eROjdiSfozUzDzvvVsIIYTf8GlSqrUuBFYBw1wuDcOsnK+UUup0oBflFzg5raikzd+11kU1i1Z88MsuRrz8EykHc3hpTG9eu7IvCbEy3cETnnjiCZ544gn3H0xJMUP3IY7/da+6ysz7/O67qp+bMcOstJ82zfpQfHg4PPqoqYv62Wfux2pVJVuMJnc2w/Y/bpHeUiGE8DtKRaLUVSj1PkptQamjKJWNUltRag5KjUOpaHeatGP4/nlgnFLqRqVUV6XUS0Bz4A0ApdR7Sqn3KnhuArBVa720gmtvAC2UUi862rwRGAc855WfIAi89v02pv5nPQPaJfL13X/hot4tZJcdD9q8eTObN292/8Ht201S6nT++RAfX/UQflYWPP00nHsunH22e+/7+9+hWzd4+GEo8dJK+Ep6Sjs2jqNp/SgZwhdCCH+iVCxKPQakAu8BVwIdgDggFlMlaTTwNpCKUk+gVKyVpn0+oVJrPU8plQhMxdQcXQ+M0FrvctxyUr1SpVQ9YAzweCVt7lBKjQBewJSWSgXu1FrXsBBkcHvjh+08+9VmLu7dnOlX9CZUdtXxuA8++MD9h7Q2PaVDhpw4FxVl5pbOmwevvw4xFey2NH26Wd3+9NPuvzM0FB5/3Lzjww/hmmvcb6M66elmbmx8fLnTztJQC9enUVxSSlhooE2BF0KIOmkb0BhwJgcZwDpOTJdMAk51HOOBKcANmA7IKtnyp7zWeobWuq3WOlJrfZrW+scy14ZorYe43J+ttY7TWv+zijZ/0Fr3dbTZTmv9hhd/hDpr5o8pTFu0iZG9mvPc5b0kIfUnBw/CsWNmnmdZV11lzn/++cnPHDgAzz8PV1xhFjDVxCWXQJ8+Zii/yAuzYQ4cMEP3FfTEJ3duRHZ+MWv3ZHr+vUIIIWqiCbAH01HYDa0bo/U5aD3G8TkHrRsD3Rz37OXkdT8Vkq4Hcdzby3bw1MKNnH9qM164opf0THnRww8/zMMPP+zeQ2XLQZWVnGzqilY0hP/UU2aRU03mrzqFhJji+jt2wKxZNW+nMmUK57safEoSIUrmlQohhLvc2dLdcf+VSqm1SqlcpdR+pdQHSqmmFdx6A9ABrR9F602VNqj1JrR+FDOcf6OVmCXrEADM/nkHT3z5J+f1aMqLo3tLQuple/bsYc+ePe49VLYcVFkhIWbu58KFZv5n2c+MGXD99dCpU+0CPu88GDTIJLf5+bVry5Wzp7QC8THh9GndkB8kKRVCCMvc3dJdKTUYU27zXaA7cDGmp/Pk3g6t30Fr63tSa12M1u9YuVWKdAo2pGbx6Bd/cm63Jrz89z6ES0Lqde+8Y+n/z/K2bzfHtm1PvnbHHZCaarYhLatfPzMntLaUMr2lf/2reVffvuWvR0eb3aDatDFbl4a7sZFaejr07Fnp5eROjXhhyRYO5xRK9QchhLDm+Jbuju/vUEoNx6y7cd2ACMzOmHu11i84vt+hlHoFeKXKtyj1HaDRemgF18xwoNaW/xKSpFTw8rdbqRcVxrOX95KE1J+lpJj96qMrqLDRqpUpeO9NZ58Nw4fDv/5V9X0hIdC8OfToYbYqHTas8jJUWlfZUwomKX3+my38tPUgF/VuUYsfQAgh6r4yW7q7ViCqakv3n4GnlVIjgS+BRMwC8+p2TxkCVLbX9aNAKZUsUq9I0CelCQkJLF261O4wbJNfVEqPkGyGnhbFml9/tjucoDFzpvnldfz48Zaf6b16NSQmstbG/17VvfcSVkHMYXl5RO7fT1R6OlGOY8Pffyfyb3/jaJcu7Bo7lkMDB56or+p87tgxziwsZFt2Nnur+Lnu61VC9o51LM3c6ukfSQghAk2YUur3Mt+/pbV+q8z3bm/prrVeoZQagxmuj8bkh98A19YoQqUaOL9y57GgT0oPHz7MkLIldoLMze+v4uftRSy776/ER7sx5CpqZc6cOQDu/bd36BAMGxY4/70WFMB771F/2jR6Tp1qhujvugsaldnLfr/ZyK3DoEF0qOLn+uzfa1i+4RC/TUkmRCpCCCGCW7HWup8nG1RKdcMM1T8BfIUp2fks8CZwjcvN1+KarJph/LLaOI5H3Ikj6JPSYPZn6lEWb9jPXUM7SkLqY2+99Vb1N5WVlwf79p288t6fRUbC+PFmu9K5c00lgBsrWYDpunjLRXKnRnz+v1Q27j9Kl6b1KSoppbCklKLiUkorGzhyER8dTkSYTE8RQtR5NdnS/QHgN631s47v1ymlcoCflFJTtNZ7y9zblvLD9gpIdmnP2Xuwwp3AJSkNYi9/u5V6kWFcP7id3aGI6uzcaY6BlJQ6hYXB2LFw5ZWwfj0UuyzajI2ttjrAWZ2SADj/5WU1DuOURrF8fvuZxEbKH3tCiLpLa12olHJu6f5xmUvDgMo2FYrBJLJlOb+v7Ld5RfnEtKxDwC/AHVZidpI/nYPUxjTTS3rn0I7Ex0gvqa/de++9ADz3nMWdcCsrBxVIQkLg1FNr9GjjelH887JT2X0ol/DQEMLDFBGhIYSHhlgazj+aV8SzX23m5W+38sBpEvQhAAAgAElEQVSIrjWKQQghAsjzwPtKqd8wi5huxmVLdwCttXNo/gtgplLqFk4M378IrNZa7y7XstaPAY9hGirFrL73yDCUJKVBytlLeoP0ktoiLy/PvQec5aACsafUQ67o16pWz+86lMPby3Ywqm9LOjet56GohBDC/7i7pbvWerZjS/fbgelAFvAdcF81r7rOk3ErrS1OyKqjYmNjdU5Ojt1h+NTGtKOc99JP3Dm0I5OG1bKouvCNiRPh7bfh6NHKyyuJKh3OKeSv05fSqXE95t10Bkr+OQohApBSKldrHWt3HAAo1Q5oBRxE641lzncFGgF70HqH1eZk1n8QeuU76SUNONu3m15SSaRqLCE2ggfO68JvOw/zyaq91T8ghBCiOq8B3wOnu5zv5zj/qjuNSVIaZPYczmXhH/u5bnBbmUtqo4kTJzJx4kTrD6SkBPZ8Uj9x+WmtOK1NQ55ZtIkjOYXVPyCEEKIqzu39FrmcX4xZ/OSy/V/VJCkNMjsPmakKZ3ZsVM2dwm+UlpqkNIjnk3pKSIjiyYt7kJVXxD+/2mR3OEIIEegaOo75Luedv/UnuNOYLHQKMgezCwBIipM9xO304osvWr95/37Iz5ek1EO6NqvP9YPbMvOnHVzm6DkVQghRI0cwc0cvB94uc/7SMtctk57SIJNxzCSljepF2hyJsKwulIPyMxPP6USz+Cim/mc9wb7YUwghauEXzDD9DJR6G6XuQal/Aa9japj+4k5jkpQGmYxjhUSGhRAnBcRh61ZYuNCWV992223cdttt1m6WclAeFxsZxq1nd2Bj2lFSMoKr+oYQQnjQS5jkMwwYB/wTUyYq3HH+BXcak6Q0yGRkF5AUFynlcADuvhtGjTJ7tPtYdHQ00dHR1m5OSTGF59u0qf5eYdmgUxIB+H3nYZsjEUKIAKX198BEoAjTY+r8FAJ3o/UP7jRnqbtMKQZoza9uhir80MFjBTJ0D5CZCV9/DUVFsGYNnHGGT19veScnMD2lrVpBhMwD9qT2SbEkxkbw244jjO7fuvoHhBBCnEzrV1DqM2A40ARIBxaj9T53m7I6hrtCKf4A/gV8oLV7E1eF/ziYXUDLhjF2h2G/L74wCSnA8uU+T0rdIuWgvEIpRb+2DVkpPaVCCFE7JgF9u9r7quHO8H0PzD6o+5TiQ6U4u7YvF76XcayQRvWkx41PPoGWLaFtW5OU+tiECROYMGGCtZudhfOFx/Vvm8Duw7mkH3WtZiKEEMISpRJQ6k6Ueg2lZrl83EpUrfaUPg9cAbQEooAxwBilSMFkxrO1Zr87Lxa+V1KqOZxj5pQGtaNH4auv4JZb4OBB+P570NqnuyUlJiZau/HYMThwQJJSLzm9nSmh99uOw4zs1dzmaIQQIsAodQqwDGhc0VXMYqcbrDZnqadUa+7VmtbAXzDL/A86XnYK8BSwWyk+VoreVl8sfO9wTiGlWspB8d//msVNl10GgwZBairs3u3TEJ555hmeeeaZ6m/c4dgyWIbvvaJbs/rERITKEL4QQtTMo5h5pKqCj9vcWn2vNcu05jagP1B2RVUYMAr4VSkuqkkgwvucNUqDvqf044+hWTMYONAkpWDLEL4lUg7Kq8JCQ+jbuiErd8o0eSGEqIFkTG/oU47vNXAhpj7pFuA8dxpzKylVimFK8SmwDdNrCiYbXgOkYOpSPVXJ48JmkpRihsMXLYJLLzVllnr0gLg4nyel1113Hdddd131N0rhfK/r3zaBTfuPkpVXZHcoQggRaJo4jtOPn9H6S+DvQCfMinzLLCWlSjFZKbYCi4GLOVEU9T9AstacBvQGjjqCEH5IthjFFMvPzzdD9wBhYTBggM+T0latWtGqVauqb9Iali6FhARoKFthekv/dg3RGlbvkt5SIYRwk3OP+6OASTKUaglkO85f5U5jVhc6/QOThCrHi2cBL2vNTucNWpOjFPuBju4EIHxHthjFrLpv0gTOPPPEuYED4ZlnTC9qXJxPwnj88cerv2nOHFO66umnvR9QEOvTqiFhIYrfdh7m7C4VzdUXQghRiQygNZAA7AXaAwtxJqjgVsLhzvD9DuBuoKXWTCqbkJbxV0dAwg8F/RajublmkdOoURAaeuL8oEFQUgIrV9oXm6vdu+G220xs//d/dkdTp0VHhNKjRTwrd8hiJyGEcNMGx7Ez8A2m87I70BfTmbnMncasJqWXAB215iWtOVbZTVqTqjW73AlA+E7QbzG6aJFJTJ1D907OwvnuDOFrbeZ7al2jUMaOHcvYsWMrvlhaCuPGmUT5/ffLJ9DCK05vl8C6vVnkF5XYHYoQQgSS54EpQC7wGLCRE6vvNwF3utOY1aR0KdBKKZLKnlSKJKVorRTx7rxU2OPgsQKSgn3oPikJ/vKX8ucbNoRu3awnpTt2wAUXmMVHt99ukkg3de7cmc6dO1d88aWXTO3UF1+UVfc+0r9tAoUlpazbm2V3KEIIETi0/g6t/4HWa9A6HbPRUh+gJ3AqWqe405zVpHQWZvj+SpfzYxzna721lPC+g9kFNArWlfd5efDll3DJJWZxk6tBg2DFiqoTzMJCM/e0e3f48UcYORJmzICbbjK9mm546KGHeOihh06+sH49PPAAXHghXH+9W22KmuvXxiwkk3qlQghRA0olotQlwI2YaZz70drtoSerSekAx/FTl/OfYbpoByD8XlBvMfr112Yhk+vQvdOgQXDkCGzeXPH1n36CPn1gyhQ47zzYuBEWLICpU+Ff/zLD7cXF1uMpKYEilxJEBQUwdizUrw8zZ/p0h6lg1zA2go6N4/hN5pUKIYR7lHoUs8jpE+ANx3EvSj3iblNWV7w0chwzXc5nuVwXfiqotxgtLYXXXzellc4+u+J7yhbR79q1/LUPPzTJYps2ZjX8BRecuPbEExAVZZLTggJzb3h45XH8+CPMmcOY2bOhqIi5MTEQH28+WpukeMECaCyrwH2tf7sEvlibSkmpJjREfiEQQohqKTUZeLiCK5HAwyh1DK2nV3C9QlaT0mygIXAuML/M+XMdx0oXPwn/4NxiNOiSUq1h0iSz1/0LL1SeMHbqZJLW5cvhhjLb9KakwC23mBJSixdDbOzJzz74oElM773XJKYTJ5a/XlhoemrnzYN9+yA2lt7du5v5rb16QVaW+WRmwo03mqF74XOnt01gzq+72Zh2lB4tZJq8EEJYcJvjmIfJD3djSkRdAkQDd1C2sH41rCalq4FzgFlK0R2zuqorMAmz5H+V1RcKewRtjdLnnjMLhyZOhLvuqvw+pUxvadnFTsXFcNVVZuenDz+sOCF1uucek5jefjt8/vnJ18PDYcQImD4dRo7k/piYmv9Mwiv6t0sA4PedhyUpFUIIa5pg8sCL0HrJ8bNKDQO+Atwa9rOalL6BSUrrY5b8H3+tI5g33Hmp8L2g3GL0gw9Mjc/Ro00yWN0czUGDzGKow4dNr+kTT8Avv8DcudC6dfXvu+02+Otf4cCB8ueVgp49ZVcmP9eiQTTN46NYufMI4wa3szscIYQIBBuBXpi97sta4Tiud6cxS0mp1nymFM9jekZdTde63JC+8EMnktIgWej09ddw3XVmDum775rezuo455X+8ouZ4/nkk3DNNSaptapr15PnpFbg0ksvBeDTT13XDgo79W+XwPLth9h2oPoZSZFhIbRKkB5vIURQmwp8DtwK/LPM+VuBIkwNU8ssb+2jNfcqxTzgQkx3bTrwudb40TY4ojLH972vC8P3RUWQnW3mYR49enI5prQ0GDPG1B6dPx8iLf7M/fubQvWLFpkFTW3bwiuveDx8gIEDB3qlXVE7Z7RPZMHaVM55/gdL9089vys3niW1ZIUQQWsyZtH7Myh1O7AHaOn4HASmoJQzMdVoPbSqxpSu4Y40dUVsbKzOycmxOwyve3rhRt5dvpNNTwwPzB2dcnLMHvXbtpmao9Vp08bMD23e3L339OsHq1aZ5HTZshO7PYmgUFBcwvebDlJYUv2GCB//voeVOw/zzd3J0mMqhPAZpVSu1rqKRQ4+pFQpZhpntXdiktIqtyi03FOqFGHACMz+ptGu17XmcattCd8L+C1GFy2CP/4wBeXbtjXD6/XrQ716Fa+oHzgQGtWgUtmgQSYpfeQRSUiDUGRYKMN7NLV0b782DRn2/A88vGA9s8b1D9z/t4QQonY89oefpaRUKRpjthqtZF9EAElK/VnAbzE6f74pofTmmxXvyOQpt95qFiQ98ID33gFc6Cj79HlFK/VFQGjeIJq7h3Xiyf9uZNH6/Yzo2czukIQQwre0troJkyVWG3sM6ILJhiv6CD8X0FuMFhaaVfEXXujdhBSgSxd47DGvv2fo0KEMHVrl1BoRAMYNaku3ZvV57IsNZOcXVf+AEEKISln9m/dczJyB2cB1jq/vwhRF1cA0bwQnPCfjWCF9WjewO4ya+e47s6DpkkvsjsRj7qqqZqoIGGGhITw9qieXzPiZ6V9v4dELu9sdkhBC+JZSIZjt5ltjdnIqT+v3rDZlNSlt4Tjej0lK0ZpXleJ74A/MKivhpwJ+i9H58yEuDs45x+5IhDhJ71YNGDugDe+t2MmlfVvSs6UU3hdCBAmlugILgFMquUMDlpNSq8P3zpo7hzB1p1CKRsAux/kJVl8ofC+gtxgtKTF7wY8YYXZMqiPOO+88zjvvPLvDEB4yeXhnEuMimTL/D0pKg7uiiRAiqMwAOlD59E63pnha7Sk9hOktjQf2Y3pGPwTyHddlqxo/FtBbjP7yC6Sn16mhe4CRI0faHYLwoPpR4Tx8QTfu+PcaXv52K3cP62R3SEII4QunYXpD/wMsBgpr05jVpHQzJik9BfgRuApwrtLQwOraBCG8K6C3GP3sM4iIMD2ldcitt95qdwjCwy44tRnfbzrAS99uJTI8hFuHdLA7JCGE8LZ0oD0wDq2za9uY1eH7mcBbQBRmJf5BTnTLZgATaxuI8J6A3WJUazOfdOhQU5NUCD+mlOLZy3txUe/m/HPxZl5fut3ukIQQwtuexuSC96JUrXu+LPWUas1HwEfO75WiI3A2UAz8rDWZtQ1EeE/AbjG6bh3s2AFT3No6NyCc41i0tWTJEpsjEZ4UGqKYfnkvtIZ/LN4EwC1DKpv/L4QQAU7rd1DqImAqcB9KHcDkhsfvQGvLfwhWm5QqRSTwp+Pb87Vmk9Ycxay2EgEg41ghkWEh1Iv0co1PT5s/H0JCTH3SOmb06NF2hyC8JCw0hOev6IXGJKZKwc3JkpgKIeogpR4ALsRM5YzgRLUmcG4t6oZqsxStKVCKRKAekOJO48I/BOwWo599BoMHQ+PGdkficePHj7c7BOFFYaEhvHBFLwCmLdrE4vX7iQit2cYnSfUiuHVIB3q0kFJTQgi/c4fjqFyONWL1T0nnGGOv2rxM2CMgtxjdvt3sdV/HVt2L4OFMTMef1Y7o8FBCQ1SNPiu2H+KCV5Zx57/XsOdwrt0/lhBClBWH6Q29BIhB6xCXT6g7jVkdz30RSAb+rRQPAmuBvLI3aM1ud14sfOdgdgEtG0bbHYZ75s83xzqalA4ZMgSApUuX2hqH8K6w0BAePL9brdo4ml/Emz9s5+1lO1i0Po2xZ7Th9rM7kBiI1TSEEHXN58DfgZVonV/dzdWxmpT+iMmEE4A5FVzXbrQlfCwgtxidPx/69IG2be2OxCvGjRtndwgiQNSPCmfy37pwzcC2vLhkC+8u38ns5TsJreF0nNAQxeMXdWd0/9YejlQIEYQ+wWxFvwilXgJ2Un6hE2j9o9XGlNbVz0FVitJqbtFa41YXrb+IjY3VOTk5dofhNSWlmo4PLuS2sztwz7md7Q7HmpQU6NABHnsMHnrI7miE8CvbDmTz+f/SKCmt7o/liv20NYOdGTn89H9/JT4m3MPRCSG8TSmVq7WOtTsOAJQqperFTBqtLXdaWr3xXasNCv9yJDfAthjNzYXLL4d69eDqq+2OxmuKiooACA+XpEC4p0PjekwaVq/Gz5/f8yjnv/ITr/+wnfvP6+LByIQQQcpjq6it1im9zlMvFL51vEZpICSlWsP48bBmDXz+eZ0dugcYNmwYIHNKhe91a16fi3u34J2fd3DtoDY0iw+w+eZCCH/i0U5LmQdaxwXUvvfTp8OcOfDkk3DBBXZH41U33nij3SGIIDZpWCe+XJfKS0u2Mu3SU+0ORwgRqLT2aKelpaRUKWZVc4vWmhs8EI/wML/aYnTrVpg5E/7+d7OIqayvvoL77oPLLquTOzi5Gjt2rN0hiCDWKiGGsWe04d3lO7nxrPZ0aBxnd0hCCOHWQqfKblS4udBJKXUrMBloBmwAJmqtf6ri/gjMFlZXA82BdOA5rfXLjuvjgHcqeDRaV1OioK4vdJr5YwpPLdzIukfPpX6UzfMXJ0+G554zX591Ftx1F1x0EezcCf37Q6tWsHw5xNX9vyBzc029yZiYGJsjEcHq0LECkp9dyuAOibx5dT+7wxFCWGT7QielZmEWMN3g+Loq5j6L3Bm+98hEVqXUaOAl4FZgmeO4SCnVTWtdWa3TuUBLYAKwFWgCuE6EygXK7eVXXUIaDA4eK/CfLUZXr4aePeHaa+HVV02vaOvWEBZmthNdsCAoElKAESNGADKnVNgnMS6S8We154UlW1i9+wh9Wze0OyQhRGAYB5QCNzi+rq5303JSanVHp3Yun47A3zBJZQ7wV6svBCYBs7XWM7XWG7XWdwBpwC0V3ayUOhcYCozQWn+jtd6ptf5Va73U5Vattd5f9uNGTHWW32wxqrVZwHTGGXDPPbBtm6lF2r497NsHH30E7drZG6MP3XLLLdxyS4X/yQvhMzee1Y6kuAj+sWgTVkbNhBDCQbl8XdnHLVZX3++q4PR2pVgBZGASyh+qa8cxDH8a8JzLpa+BQZU8djGwEpiklLoGs5PUImCK1vpYmfuilVK7gFDMjlMPaa3XVBdTXec3W4zu2gVHjkDfvub70FC4+GLzKSqCICuNNHr0aLtDEILYyDDuHNqRhxdsYOnmg5zdpbHdIQkh/F+7Sr6uNas9pZUJw3TbDrd4fxImaUx3OZ8ONK3kmfbAmUAv4FLgdsf7Zpe5ZzNwPXARZrurfOBnpVTHihpUSk1QSv2ulPq9uLi4olvqjIxjhTTyh0VOaxy/H7gucIKgS0gBsrKyyMrKsjsMIRjTvzVJcZEsWLvP7lCEEIFA611ovavc11V93FCb1fdRwGAgEvDm364hmMT3Sq11lolH3Q58pZRqorVO11qvAFaciFctx/SW3gHc6dqg1vot4C0wC528GLvtDmYX0LtVvN1hmPmkoaFwqpSfAbjooosAmVMq7BcRFkLrhGgOOip1CCGEXayufhlHxRNZnfMFFlpsJwMowSxUKqsJUNkc0DRgnzMhddjoOLbm5F5XtNYlSqnfMXNfg1ZJqeZwToF/FM5fswa6dIFoKdQNcOedJ/2uJIRtkuIi2XUo1+4whBBBzp3h+4omsBZghtEnWmlAa10IrAKGuVwaBiyv5LGfgeZKqbLLsjs5jhV2CyuzqudUTEIbtPxqi9HVq0/MJxWMGjWKUaNG2R2GEIBZiZ8hPaVCCJtZ7SmtaCJrgdaV9m5W5XngfaXUb5iE82ZM7dE3AJRS7wFora9x3D8HeAh4Ryn1KNAAU1LqE631AcczjwC/YMpF1ccM2Z9KJSv6g4XfbDG6fz+kpVU8nzRIZWRkAJCUlGRzJEJAo7gIDucWUlxSSlhobZcaCCFEzdRm9X2NaK3nKaUSMcXwmwHrMeWenO9o7XL/MaXUOcArmFX4R4D/APeXua0BZo5oU8z81jXAX7TWv3kq7kCUftSUabV9i1HnIifpKT3usssuA2ROqfAPSfUi0RqO5BbZ/+eFECJoWV3oNBw4HVijNV+UOX8h0Bv4TWsWW32p1noGMKOSa0MqOLcZOLeK9u4G7rb6/mCQU1DMtEWbqB8VRke7txB0JqW9e9sbhx+555577A5BiOOcoykZxwokKRVC1JxS0WidV9PHrQ7fPwwMAM5zOX8MeBSz8t1yUiq8S2vNvR//jy3p2bxz3ek0jLW5JNTq1XDKKRDvB1UA/MTIkSPtDkGI48ompUII4RZTfvNZzPqgSCAMpV7ETKecjtYbrDZlNSnt4jiucDnvHB7vavWFwvte/W4bi9bv58ERXUnu1MjucExPqQzdl7N/v5mO3bRpZeV5hfCdREctY0lKhRBuUaoNJjdsiFkA76zUVARci1lw/qDV5qzOaI9xHF3Hgeu5XBc2+3rDfqZ/s4VL+rTgxrP8YNvOzExISZGk1MWYMWMYM2aM3WEIAZTpKc0utDkSIUSAeRRIwCShZX2CSVLPcacxqz2laZgFSA9idlRymuI4prrzUuEdW9KzuXveWk5tGc8zo3rav989wNq15igr78u5//77q79JCB+pHxVGRGgIGTnSUyqEcMu5mN7RvwHflzn/h+PYxp3GrCalS4AbgFuU4lzMtp6dgVMcwSxx56XC8w7nFDL+vd+JiQzjrav7ERUeandIxurV5ihJaTnDh1vdmVcI71NKkRQXIT2lQgh3OecIutaad/aKNXSnMavD99Mwi5rAJKIjHEcF5DiuCxtorVn4RxrnvvAjaZn5vDG2L03jo+wO64Q1a6B5c2jiuolXcNuzZw979uyxOwwhjkuqJwX0hahLlFK3KqV2KKXylVKrlFJnVXN/hFLqccczBUqp3Uqp6rYfPOw4tnU5f6HjeMidmK3WKd3u6CF9m/KLmv4EbtSaFHdeKjxjf1Y+Dy1Yzzd/ptOjRX1mX9efHi38bIW77ORUoauvvhqQOqXCfyTGRnAgW5JSIeoCpdRozEZDtwLLHMdFSqluWuvdlTw2F2gJTMBsRtQEqG5v8BWYBHROmZe/CVyDGUlf5k7cVofv0ZpfgO5KcYoj0HSt2e7Oy4RnlJZq5vy2m38s2kRRaSlTRnTh+sHt/G8nltxc2LQJLr3U7kj8ztSpU+0OQYhykuIi2ZiWbXcYQgjPmATM1lrPdHx/h1JqOGanywdcb1ZKnQsMBU7RWmc4Tu+08J5/ABcAfTmx8v5GzEh6CTDdnaAtJ6VOjkRUklEbzfwphWcWbWJwh0SevqQnbRJj7Q6pYuvWQWmp9JRW4Jxz3FqQKITXJdWL5FBOAVpr/1gkKYSoEaVUBHAa8JzLpa+BQZU8djFm18xJSqlrgDxgETBFa32skmdA619Q6irMhkgJZa4cAW5D61/did3qjk4fAmOAR7XmiTLnH8KUA5ijNVe782J/kZCQEHBDqNGHc3mgTymdm+Sz44+V7LA7oEo0X7CATsCKggIKAuyfsbelppqCFc2bN7c5EiGMU4oLubNbEd99v5TQEElKhfBjYUqp38t8/5bW+q0y3ycBoUC6y3PpVF6iqT1wJlAAXIrZvv0VoDlwWZXRaP0RSn0BDAYaAweA5Wida+mnKcNqT6kzs/7A5fz7wGOYHyQgHT58mCFDhtgdhlsufu1nYiNDuWn0GXaHUrUPP4SEBAZecQVIz0s5zv/mAu0XIlF3LVi7j+nL1rJkWD86NK5X/QNCCLsUa637ebjNEMzw+5Va6ywApdTtwFdKqSZaa9cE11BqFqDR+gZcKzGZHlfQ+j2rQVhNSps5jvtdzjuDlG1pfCg1M88/dmqqjnMnJ0lIT/LYY4/ZHYIQ5TgL6B/MLqRDY5uDEULURgZmPqdr2ZsmnJzHOaUB+5wJqcNGx7E1J/e6Oo3DJLM3VHBtNlAKWE5Kra6MyXccB7qcH+hyXXhZYXEpB48V0KxBdQvibFZUBH/8IfVJK5GcnExycrLdYQhxnDMpPSQF9IUIaFrrQmAVZi/6soZxcj1Rp5+B5kqpsjt3dnIcd7kdhFLOnT7d6pWy2lP6B2auwGylmILJnrsCT2Ey5D+qeFZ4UPrRfLSG5v5Ui7Qif/4JhYWyyKkSmzdvBqBz5842RyKEkRQXAUCGlIUSoi54HnhfKfUbJuG8GTM/9A0ApdR7AFrraxz3zwEeAt5RSj2KmVP6EvCJ1vpAuZaVugi4yOXcLJf3d3Qcj7oTtNWkdDYmKW0BvFs2DExSOtudl4qaS83MA6C5v/eUyk5OVbrpppsAmVMq/EfDmAhCFGQck12dhAh0Wut5SqlEYCpmCuZ6YITW2tnr2drl/mNKqXMwi5tWYlbP/weoaE/s3pwYtgeTC15bURjAWnfitlo8/22lGI5ZkeXqE61xzZCFl6RlmZkSzRv4cU9pWhq88grEx0PHjtXfH4Sefvppu0MQopyQEEVCrOzqJERdobWegSnVVNG1IRWc24zZy94qZ8ek82tXG4CJbrTnVvH8y5XiCmAkjuL5wOda87E7LxS1k5plekqbxftpT+maNTByJGRmwr//DSF+VtDfTwwaVFmpOCHskxQXIUmpEKI6L2JGyBWQgklM25W5roFDaJ3jbsNuFc/Xmo+Aj8qeU4o44FKtyw3rCy9Jy8ynflQYsZFu73vgffPnw9ixkJgIy5ZB7952R+S31q9fD0CPHj1sjkSIExrVi5TheyFE1cwKfbNKX6nHMSWh3F8MVYEaZTZKEQIMB67G9JxGgSSlvpCamed/80m1hmnTYMoUGDAA/vMfaCpVwqpy++23AzKnVPiXpLhIdmS43bkhhAhWWj/qyebcSkqVoj8mER2N2TEAys8pEF6WmpXvf0npE0/AI4/A3/8Ob78N0X4Wnx969tln7Q5BiJM4h+9lq1EhhGVKjQUmAZ0xnZRlabS2nGtWe6NStAPGAldxYol/2T+t8jArtIQPpGXl0bd1A7vDKG/xYjjjDLODk/xFZkn//v3tDkGIkyTGRZJfVEpOYQlx/jhFSAjhX5S6AlMcX+NmTdKKVPqnjlLcjElGyxbMd32hBppozbHaBiKql1tYTGZukf/1lO7dC2efLQmpG9auNVUyesu8W+FHnAX0M7ILJHArcmAAACAASURBVCkVQlhxm+OYB8Rg8sLDQCKQ6fhYVtXS6BmYhFQ5PkXAQsxWUkOcN0lC6jupmX5YDqqkBFJToVUruyMJKBMnTmTiRLcqZQjhdc4C+rKrkxDColMxieg5x89o3Qh4BJM3jnSnMSu/CmtgFjBZa5PxKkV3d14iPCPNH8tB7d9vEtOWLe2OJKC8+OKLdocgxEmcPaUHs2UFvhDCkljHcTXO9UVKhQLTgceAl4GhVhuzOj5zPTBSKeYDnwIZVl8gPCfN0VPawp+G7/fuNUfpKXWLDNsLf9SonmP4XmqVCiGsOQo0xIyoZwP1gPNwloyCAe40VtXw/TRgDyeG7xsDE4CvgGVuhSw8Yl9mHkpBk/p+NHy/Z485Sk+pW1auXMnKlSvtDkOIchJizfC9JKVCCItSHcfGwEbH1wuApY6vD7vTWKVJqdZM0Zq2mPmj/8Jkvc4E1TmZFaXYqxTT3HmpqJm0rDyS4iKJCPOjXZKkp7RGJk+ezOTJk+0OQ4hywkNDaBATLkmpEMKqNZi8cABmFb4q8wE3a9hXO3yvNT8CPyrF7ZgJq2OBEUC445bmwGTgfndeLNyX5o81SvfsMXVJGza0O5KA8uqrr9odghAVSoqL5JDs6iSEsOZW4P+AbLTORal4TC37YmA+8A93GrNc80NrCjHzST9ViobAGEztUtnE20dSM/Po1KSe3WGUt3evGbqXclBuke1Fhb9yFtAXQogqKRUJXOr47msgF62nQc1Hz2s0Dqw1R7Tmda05E+gAPFrTAIQ1WmtSM/P9a+U9mJ5SGbp32/Lly1m+fLndYQhxkqS4SDKkp1QIUR2tCzDTO2dhyj/VWq2rI2tNCvCEB2IRVcjKKyKvqMS/apTCicL5wi1TpkwBYOnSpfYGIoSLpLhIMrKlp1QIYUkKZrfPUk80Jlt2BIgThfP9qKdUCufX2Jtvvml3CEJUKCkuguyCYvKLSogKD7U7HCGEf5sOvAncA0ytbWOSlAaIE4Xz/ainVArn11jnzp3tDkGICjkL6B/KKfSvmshCCH80CDgEPIBSo4D/YbYcddJofYPVxiQpDRCpmebfsV/1lEo5qBr74YcfAEhOTrY5EiHKcyalGdkFkpQKIapzLc6dnKCz4+NKktK6JjUrn/BQRSPHXxh+QQrn19gjjzwCyJxS4X+SZFcnIYR7qiq/o6u4dhJJSgNEWmYeTepHERLiR6WXpKe0xmbNmmV3CEJUKFF2dRJCWNfOk41VmpQqxV/cachRZF94SWpmPs39sRyUFM6vkfbt29sdghAVanS8p1TKQgkhqqH1Lk82V1VP6VKsd7vqatoStZSalUe/Nn6W/O3da3pJpXC+25YsWQLAOeecY3MkQpQXFR5KXGSY9JQKIaz7//buPD6uut7/+OvTNGnSJd1S2lJayiaLoEUWBVEqm4CyI+31YoX7wwoFBBEUuCA7VFkuRUWkCmXxXioFBJSyShEB2UEKGKBNW1q6703SNsvn98f3DJmGLDPJzJxJ5v18POZxZs75nnM+M5O0n3xXswGEqaE+W3vmnnKlZXuJpLKNPNDY6Cxdt5Hh+Tbo4OOP1Z+0g66++mpASankp7Cqk2pKRaQdZsXAbcAEWl6QKa1Ky7YK3tXs9WHAMOAFYCGwDfBVYAXw11RvKOlbsWETdQ3O1vk0HRSEmtKDDoo7ii7pnnvuiTsEkVYN1gT6IpKa84FTM3WxVpNS96abmPGfhCx4nDszkvafBPwfIVGVLFm0JjFHaR7VlCYmzldNaYeM1OAwyWMVfUuYu7w67jBEJP+NJ9SGvg2MiZ4/BBxJqMD8RzoXa6mqtSWJWfofb7b/MUIT/wXp3FTSs3htHq7mpInzO+Xxxx/n8ceb/zqJ5IeKvr1YWa3mexFp1w7R9sRP97ifCHyHMDL/0XQulmpSOjraTmq2/8xou206N5X0NE2cn0fN95oOqlMmT57M5MmT4w5DpEUVfXuxumYz9Q0ZWc5aRLqv4mg7H2gAwKwMeBooAq5I52Kpdj79ANgduM6MnwCLgeFABaGq9oN0birpWbx2I2XFRfQvK26/cK5o4vxOue++++IOQaRVFf164Q6rqjezVXke/TEsIvlmNTCEMOp+FSEvvBTYEB3fMZ2LpZqU/jehj0BRdMOKaL8BjcDF6dxU0vPJmlq2HlCK5dPUS6op7ZRhw4bFHYJIqyqiCfSXb9ikpFRE2jKXkJSOAN4Avgn8LDrmQFU6F0up+d6dvwCHAy9HN7Fo+0/gMHeNvs+mT9ZuzK/+pKCJ8zvp0Ucf5dFH0+pqI5IzFZpAX0RS8xShtXwX4AZCRaXRNKXolelcLOW5o9x5BnjGjN7AQGC1OzXp3Ew6ZvGaWnbeeUjcYWxJE+d3yo033gjAUUcdFXMkIp9V0TckpSs1gb6ItMX9MuCyT1+bHUgY9FQP/Bn3tGZnSmsVJjN6EvqWDnZnZjrnSsdsrm9k+YZN+VlTqv6kHTZjxoz2C4nEpKJvaL7Xqk4ikqZX0k1Ek6U6+h4zvgMsAl4iGuJvxjNmzDXjsI4GIG1bum4j7uTfuveJmlLpkIqKCioqKtovKBKDvr160qtnDzXfi0j7zHbDbAZma4GNmK3B7H7Mdkv3UiklpWZ8jTBJfgVb9hX4K2G6qBNbPlM6KzEd1PB8mg5KE+d32oMPPsiDDz4YdxgiLTIzKrSqk4i0x2xfwnij44B+hPywHDgeeBmzfdK5XKrN9xcREth/EzqzJvyV0LF1v3RuKqnL64nzVVPaYbfccgsAxx9/fMyRiLSsom8Jf31nMS9XrWq37LaDe3PP//syRT3Ux1ykwNwE9Ime1wErgcGE+Uv7RMe/lurFUk1Kv0IYbX8U8GHS/rnRdkSqN5T0JJYYzavm+8R0UKop7bCHH3447hBE2nTmN3bkyfeWtltuxYZNzKpczqvzVvGV7QfnIDIRySN7EfLDXwEX4V4bTZ7/C+Cs6HjKUk1KE1nwgmb7B0TbPMqYupfFa2sZ0LuYspKiuENpoonzO61///5xhyDSpsM+P4zDPt/+fLrVm+r50lVPMfOdxUpKRQrPamAocCnuoRYtJKaXEJLSlelcLNWBTouibfNm+vOj7cJ0biqp+2jZBrYZmGc5vybO77Tp06czffr0uMMQ6bQ+vXoyduchzJy9hMZGjzscEcmtu6PtLs327xxt70jnYqnWlD4B/BD4c2KHGf8GdiJU2z6Rzk0lNWtr6nh13momfn37uEPZkibO77Tf/va3AIwbNy7mSEQ678g9hvPEu0t5Y8Fq9h49KO5wRCR35hBqQx/FbCqhRX0UcBqhQnM+ZhM+Le1+d0sXSUg1Kb2aMMJ+MCEJhZCQWhTMdanHL6l6tnIZDY3OobsNjTuULWni/E577LHH4g5BJGMO2mUrSop68Ng7S5SUihSW39GUF7a05PzUpOdOU81qi1JdZnQR8FXgSZqWkGqMXn8tOi4Z9tR7SxnSrxdjthnQfuFc0sT5nda7d2969+4ddxgiGdGvtJivf66Cx2cvxl1N+CIFxtJ4tCmdZUY/AA43oxQYBKxyZ2PaoUtKNtU3MKtyGUeP2Zoe+TbNysKFcNBBcUfRpd17770AnHzyyTFHIpIZR+w+nKffX8bbC9cyZmSe/SEtItlyaiYvllJSakZ/oD9Q484K4JNofwXQG1jrztpMBlboXpqzkurNDfnXdK+J8zPi97//PaCkVLqPQ3YdSnGRMfOdxUpKRQqF+12ZvFyqo+/vAKqA7zbbPz7a/4dMBiWh6b53SRH775BnS1Fq4vyMeOqpp3jqqafiDkMkY/r3LuarO1bwmJrwRQqXWQ/MxmF2AWZj0j091aT0y9H2gWb7HyT0EfgyaTCzSWZWZWYbzex1M2tztn8zKzGzK6NzNpnZAjP7UbMyJ5jZe9Hx98zsuHRiyieNjc5T7y3lwM8NobQ4j+YnBU2cnyHFxcUUFxfHHYZIRh25+3A+XlXLu5+sizsUEckFs8mYLcPssmjP/cD/ApOBVzE7OJ3LpZqUDom2a5rtX9vseLvMbBwwBbgW2BN4EZhpZqPaOO0+4HBgImHuq+8A/0q65n7AdOCPwJhoe7+ZpZUs54t/LVrLsvWbctd07w5Tp8KTT4Za0LYkJs5XTWmnTJs2jWnTpsUdhkhGHbrbUIp6GI+9szjuUEQkNw4kzMz0d8xGAMfRNKipCLgwnYulmpSuj7aHNdufeL0hjXueB0xz96nu/r67nw0sBs5oqbCZHQYcDBzp7k+5+zx3f9ndZyUVOxd41t2via55DTAr2t/lPPXeEop6GAftslVubvjWWzBxInzzm7DttnDxxVBZ2XJZ1ZRmhJJS6Y4G9ilhv+0H89g7asIXKRA7RNt3gX2i5/fSNABqz3QulmpS+gYh673DjEvMOMGMSwh9SR14PZWLmFkJYR3UJ5sdehLYv5XTjgVeBc4zs4Vm9qGZ3WJmfZPK7NfCNZ9o45p57an3lrLP6IEM6F2Smxs+8kiYc/SOO2DMGPjlL2GXXWC//eC222DVqqaymjg/I2bNmsWsWbPiDkMk447YYxjzVtbw7yXr2y8sIl1dYs3sVYRVnRx4lNCED1CezsVSTUpvS7r4FcCfou2AZsfbU0Gozl3abP9SoLVFlrcHDgC+CJxAWEv1cGBaUplh6VzTzCaa2Wtm9lp9fX2KoefG/JXVfLB0A4fu1v6a0xnzyCOw//5w6qnwl7+ExPP662H9ejjjDBg+HI4/Hv78Z5g7VxPni0irDtttGD0MZqoJX6QQJGqtjqOp9fwDoF/0PK0O5qlOnv8gcBMtT4J6ozsPpXPTNPUgZN7fjZrtnyAkpieYWYc6Xbr77e6+t7vv3bNnylO15sRT74Xc+rBc9SdduBDeeAOOPrpp3/DhcP758M474dikSfDCC3DccSExVdN9p02dOpWpU6e2X1CkixnSrxf7bjeIx2YviTsUEcm+t6PtfYT+peuB2YQKRQjLjqYs1ZpS3DmfMMr+GuD30fbL7vw0jfutABqA5hnXUKC1f8EWA4vcPXke1PejbWJw1JI0r5m3nnxvKbsM68fIQTla7efRR8M2OSlNMIM994T/+R9YtAgeewwmTIDTTstNbN3Y9OnTmT59etxhiGTFt/YYzkfLNvD8h8vjDkVEsmsysJGmyspf4t4AfDs6/mI6F7Ncd0Y3s5eBt919YtK+D4AH3P2iFspPBG4GtnL3DdG+g4GngaHuvszMpgMD3f2wpPOeBFa6+3+0FU+fPn28uro6E2+t01ZVb2bvq5/irG/syHmH7Zybmx5xBMyZEwY2qUleRDJgY10Dh9/8dwAeP/fr+Te1nUgXZmY17t4n7jg+ZTaSMMipCvc3o327Elb/nIt7yn15Um67NqMfcCSwLVDa/Lg7V6Z4qZuAe8zsFeAF4HRga6J+qWZ2d7ieT4jK/y9wKXCnmV1O6Mc6BZjh7suiMlOAv5vZhcCfCX0bvkHoi9plPPP+Uhqd3PUnXb8e/vY3OPtsJaQikjGlxUVcc9we/OfvX+Y3z37ET3L1R7aI5J77x8DHzfa933LhtqW6zOg+wGOErLc1KSWl7j7dzAYDlwDDCX0PjnT3+VGRUc3KbzCzQ4BfEUbhryYknhcmlXnRzMYDV0dxzAHGufvLqcSUL55+fynD+5ey+4i0Bqt13BNPwObNcMwxubmffOrWW28FYNKkSTFHIpIdX92xguP3HMFtz83h6C9uzU5D+7V/kojkP7NQaeh+96fP2+J+d8qXTqX53owXCNMutX5Lp0u2z+RT8/2Xr32ar+5YwU0npb0yV8dMmBD6iS5ZAnk24Ku7O+KIIwCYOXNmzJGIZM/KDZs4+Kbn2GmrvkyfuB89eqhFRqSzYm++N2sEGnHvGT1vK5F03FNOMFIt+IXops8RlhqtbicISdP6jXUsXbeJHbfq237hTKivh7/+Fb79bSWkMVAyKoVgcN9eXHzkrvx0xr/402sfM37fthbuE5EuxFp53impZiNrgN7A8e6fWWpUMqBqRait3WFIjpLSF18Mk+K3NOpeRCRDvrPXNsx4fSHXPvY+B+86lCH9esUdkoh0zqmtPO+0VJPSuwl9OHcH/pHJACSYszys1LrDkBzVyD/yCJSUwGHNV46VXJgyZQoA55xzTsyRiGSXmXHtcXtw5JTnueov73HlMZ/P+j379upJz6KUZzwUkXS439Xi8wxINSmdB6wFHjbjD0AlUJdcwJ2UO7LKZ81dXk1RD2PUoBwkpe7w8MNw0EHQT4MP4vDMM88ASkqlMOy4VV/OGLsDU575kEfe/iTr99tn9EDuP71LrjItUtBSTUp/R1Mf0p+0cNxBSWlnzFm+gVGDelPSMwd/3VdWwkcfwXnnZf9e0qJHHnkk7hBEcuqsg3Zk28G9WVtb137hTpg5ewkfLduQ1XuIFDSzuWmUdtx3SLVwOiNcNGwyi+Ysq85t0z3AUUfl5n4iUvCKi3pw/Jeyv0Txig2beGP+atwd0/zLItkwmi0Huyd+0ZoPgLcW9rUp1aQ0ox1ZZUsNjU7VymoO3HlIbm748MOw115awz5GN9xwAwDnn39+zJGIdC/lpcXUNzq1dQ30LtHMIiJZ0tJffJ3+KzCl31h3MtqRVba0aHUtm+sbc1NTunw5vPQSXH559u8lrXrppZfiDkGkWyovKwZgbW2dklKRbHBv6mdotgPw9+jx38BCYBvgWsLKmgemc2n9xuaBOStC/6ftczEd1LvvhoFOB3SpFVi7nQceeCDuEES6pfLSkJSuq61neP+YgxHp/m4BhgFn4J6YMnQuZqcDq4CbgcNTvVjKo2rMONmMN8yoNqOh2aM+nXcgW5qzLDEdVA6S0oULw3bkyOzfS0Qkx8rLQl3Luo3ZHVAlku/MbJKZVZnZRjN73cy+luJ5B5hZvZnNTqF4oiZ0+2b7d4y2adWApVRTasZJhNH1jgY8ZdzcFdUM7F3MoD4l2b9ZIikdMSL795JWTZ48GYALL7ww5khEupemmlIlpVK4zGwcMAWYRJhffhIw08x2c/cFbZw3kJDvPQOkkihsAMqAmZjdQ1Pz/feSjqcs1eb7M6NtLWFlJydUyw4mrPakVZ46Yc6yDblpuoeQlA4aBL175+Z+0qK33nor7hBEuqVEn1LVlEqBOw+Y5u5To9dnm9nhwBnARW2c9wfgLkIF5Ikp3OcewlShFcCPk/YnRt6nNV1oqs33X4gufkhihztDgMsIk+hrbqFOmLM8h9NBLVyoUfd54L777uO+++6LOwyRbqe8NGq+r1WvMilMZlYC7AU82ezQk0Crq0qY2SRgKHB1Gre7mJB4WrMH0f6L07hWyjWliYzpDaI5p8woAm4EriB0dD04nRvni0GDBjFr1qzY7t/gzve3q2ZYaQOzZq3O+v32ev99Ng8cyDsxvmcRkWxx4Cd71FO2spJZs+bFHY5INvQ0s9eSXt/u7rcnva4AioClzc5bSlLlYjIz24NQ0fgVd29IeY5f9zrgFMyuI4y2HwysAGbhXpnaRZqkmpSuAwYSst/1QD/gCMLSowBfTvfG+WLVqlWMHTs2tvu/uWA1Nz7xIlMnjGHsbkOzf8O1a+HAA2N9zwJXXXUVAJdeemnMkYh0P5MufZyTvzKSk8buFncoItlQ7+57Z+piZtYLmA6c7+5VHbpISEDTTkKbSzUp/YSQlG4FvA/sCzycdHxVZwMpVHOXVwOwfS6a7zdvhqVL1XyfByorO/27KyKtKC/rqeZ7KWQrgAZCU3yyocCSFsoPB3YF7jSzO6N9PQAzs3rgSHdv3hWgZWEJ0rSWFk2WalL6JrA7oUb0bj5bM6rJ9TtozvIN9OxhjBqUg4FHn3wStkpKY3fvvffGHYJIt1VeWqyBTlKw3H2zmb0OHArcn3ToUKClSbIXAXs02zcpKn8cMC+N248mzaVFk6WalE4Cfgqsd6fGjP7AOKAeeAj4RUcDKHRzl1czanBviotSnjK24xLTQSkpFZFurLxMSakUvJuAe8zsFeAF4HRga+A2ADO7G8DdJ3joF7rFnKRmtgzY5O6pzFWaMakuM1oNVCe9ngxMzlZQhWTO8g25mTQflJTmkZ///OcAXHnllTFHItL9lJf2ZMWGzXGHIRIbd59uZoOBSwjN87MJzfDzoyKjYguuDa0mpWbpBexOq5OxSsvqGxqZt7Kag3fNwQAnUFKaRz7++OO4QxDptsrLipm7orr9giLdmLvfCtzayrGx7Zx7OXB5xoNqR1s1pfNIvV+At3MtacHC1bXUNXhuBjlBSEr79YPy8tzcT1p15513tl9IRDqkvLRYKzqJxMG9U30R20sktaRoFs1ZnsM17wEWLVItqYh0e+VlPVm3sR53J+X5FkUkdm0lpRpRn2WJ6aC0mlPhueiisMrbddddF3MkIt1PeWkxDY1OzeYG+vRSI55IzoX+rMuBRtxT/iVstaA7p2YiLmndnOUbGNynhAG9S3Jzw4UL4ZAWF3OQHFu5cmXcIYh0W+VlxQCs21inpFQkXmk1Vei3NUZzl1fnrj9pfT0sXqya0jxx++23t19IRDqkvDRKSmvrGd4/5mBEuhuzSSmU6lByk3JSasbOwA+BnYGyZofdnYM7EkAhm7N8A4fmYmlRCCs5NTQoKRWRbq+8LPzXprlKRbLi13Rigvy2pJSUmrEXMAtoadkhI0vBdWdrajazsnpzbkfeg5LSPHH++ecDcMMNN8QciUj301RTqqRUJIsyPoow1ZrSi+lgVay0bM6ng5w0cX4hqq2tjTsEkW4ruU+piGTcZqCYsDrU0lbK9AYuSPfCqSal+xNqQycBv42efxG4GtiFsOSopCHn00EpKc0rv/nNb+IOQaTbKi+Nmu9r62OORKRbegvYB3gW9/tbLBFG36edlKY6yengaPvHxA53ZgMTgc8BP073xoVu7vJqiouMbQY2756bJQsXQmkpDBqUm/uJiMSkn5rvRbLpZULT/ZczfeFUa0prgb7Axuh5aTTwaUN0/OhMB9bdzVm+gdGD+9CzqFOLH6QuMUepJpLOC+eeey4AN998c8yRiHQ/JT17UFZcpOZ7key4CrgDWNNGmVXAduleONWkdBkhKR1EWH50F+BZINE20pjujQvd3OUb2HGrHDXdgybOF5GCUl7WU833ItngvgJY0U4ZB+ane+lUk9J3gO2BLwB/AXYFEnMZOfBkujcuZA2NzoJVNRy627Dc3XThQjjggNzdT9qkGlKR7CovLVZNqUgXk2rb8RXAdwm1pFcTktBEO/AzwDkZj6wb+2RNLXUNzujBLc2wlQWNjWHd+xEjcnM/EZGYlZcpKRXJCrPLMBuQRvkBmF2WStGUklJ33nZnujsfubPencMJTfn93TnMneUpBycsWFUDwKhBOUpKV6yAujo13+eRM888kzPPPDPuMES6rfJSNd+LZMllwHzM/oDZYZh9dspQsz7RsTsIzfg/T+XCnVlmtASo7sT5BWv+yigpzVVNqaaDyjtlZTmadUGkQPUvK2buCv0XJZIF7wG7AadEj0bM5tHUz7QCGE1TxacB76Zy4TaTUjO+BIwHSoE/u/M3M04DriPUlG4y41Z3zk/9vcj8VWE6qOH9czgdFCgpzSNayUkku8rLijUllEh2fAH4f8D5wE5AEbADYewRbLnS0xzgeuD3qVy41aTUjAMI/UUTZc4043rgp4TBTUZIVn9sxkfu3Jbquyl0H6+qYeTA3hT1yNH0TEpKRaTAhIFO9bg7pqnwRDLHvRGYCkzFbCzwTcJk+onR20uBV4EncH82nUu3VVN6AWEZqeb7ICSkKwhVtAZ8D5SUpmr+yprcNd1DSEp79oSttsrdPaVNEydOBOD222+PORKR7qm8rCcNjU7N5gb69OpMTzURaZX7LGBWpi7X1kCnvQk1ok8QlhedSUhAHfgPd7YC/jMqu1umAuru3J0FK2vYNleDnCAkpSNGQI8cTdQv7Ro8eDCDBw9uv6CIdEh5YlUnjcAX6TLa+vOxItqOc2edGf8HrI72PRhtHyAsPdovS/F1O6tr6li/qZ6RuU5K1XSfV6677rq4QxDp1srLEkuN1jO8f8zBiHRXZvsSKjHfx/1ZzA4FbgFGAY8DE3BPecRhW1VnxQDurIu2axMH3KmLtpsTYaXzHgrZ/JXhu9l28GdnUMgaJaUiUmBUUyqSEz8FfgV8DrNiQkXl54Ay4FjC9FEpa7ejjdln55ZqaZ+kJjFH6ba56lPqHpLSo4/Ozf0kJaeeeioAd955Z8yRiHRP5WXhv7e1NUpKRbJoz2j7N2AvQiv7YuCT6PUxhMQ1Jan0/k7Ocr2FfZKGBStzPHH+6tVQW6vVnPLMyJEj4w5BpFtTTalITiSWnP8YODB6Phn4EyE53Tadi7WXlKpZPsPmr6phaHkvSouLcnNDTQeVl6688sq4QxDp1pr6lCopFcmiRGVlb2D36PW7NI1BakjnYm0lpVekHZq0K4y8z3F/UlBSKiIFpV9p+O9t3UYtNSqSRYsIE+g/StNMTO8CW0fPV7R0UmtaTUrdlZRmw/xV1Ryw45Dc3VBJaV46+eSTAbj33ntjjkSkeyou6kHvkiLVlIpk10PAz4CvEFrXX8F9KWYnRcf/lc7FNKNwDm2sa2Dpuk25G+QEsGhRmJ902LD2y0rO7LzzznGHINLthVWdlJSKZNEVQDnwNaAKOC/aP4qwKuh96VxMSWkOdWjkfWNjePTs4Fe1cGFISIubL84lcbr00kvjDkGk2ysv68m6WjXfi2SN+0bgzBb23wDckO7llJTm0PyOjLz/7nfho4/gxRehpCT9m2qOUhEpUKopFckRsz2AQwhTQq0AnsJ9drqX0bqTOdRUU5riQKc334Tp0+H11+GGdv7gePNNOOccWL58y/1KSvPS+PHjGT9+fNxhiHRr5WVKSkWyyqwnZtOAtwg1oxdG27cxuxOztKYaUlKaQwtWVtOvV08G9k6x5L3RvgAAIABJREFUKf3aa6G8HI44Aq66CubMabncypVwzDFwyy3wpS/BSy81HVNSmpfGjBnDmDFj4g5DpFsrL1XzvUiWXQ1MIAxyav6YEB1PmZLSHJq/qoaRg3pjlsL0r++/Dw88AGedBVOnhj6hkyaFFZqSNTbChAmwdCnceWco9/Wvw5QpsG5deCgpzTsXXnghF154YdxhiHRrqikVyboJhLlJlwHXAqdH22WExPSUdC6mpDSHFqysSX2Q0+TJUFYG554bVmO6+mp48snQnJ/shhvgscfgppvglFNCU/+RR4bzvv3tUEarOYlIASovLWZdbR3e/I95EcmU/tH2W7hfgvvtuF8CHBXtL0/nYkpKc6Sh0fl4dQ2jUklKq6rgj3+EiRNhSDSn6Zlnwl57hWRzzZqw7x//gIsvhu98J9SiAgwcCA89BL/4BbzwQtinmtK8c8IJJ3DCCSfEHYZIt1Ze1pNGh+rNaS0qIyKpey3afthsf2W0fSWdiykpzZHFa2upa/DUVnP65S+hqAjOP79pX1ER3H57GMh08cVhO348jB4dmveTuwT06AE//Sn87W9w2mmw994Zfz/SOfvttx/77bdf3GGIdGvlpVpqVCTLfgxsAK7GrBQg2l4FrKNp3tKUaEqoHEl5jtJPPoE77ghN8c2b3b/0JTj77DCg6eWXQ2L6z39C//4tXooDDwwPyTvnJ//BISJZUV4WJaUb69iaspijEekmzOY230OYq3QiZiuBwUAxUA3MAHZI9dJKSnNkQapzlN54IzQ0wM9+1vLxq66CGTPgjTfg1lthzz0zHKmISPfQVFOqEfgiGTSaMLgp0USbeF4CDE/a1xdIcQ7MQElpjsxfVUPPHsbw/qWtF1qxAm67Df7jP2D77Vsu068fPPhgmPbp9NOzE6xk3dFHHw3AI488EnMkIt1XeVn4L07N9yIZtYCQdGZcLEmpmU0CLiBk1O8C57r7862UHQs828KhXd3931GZU4A7WyhT5mEJrNgtWFnDNgPL6FnURjfeKVOgpgYuuqjti+27b3hIl3XwwQfHHYJIt/dpTammhRLJHPfR2bp0zpNSMxsHTAEmAf+ItjPNbDd3X9DGqZ8HViW9brZ0ETU067eQLwkpwPxV1YxqbyWn+++Hww+H3XbLTVASm3POOSfuEES6vU/7lKqmVKRLiKOm9DxgmrtPjV6fbWaHA2cAbVURLnP3FW0cd3dfkqkgM8ndmb+yhj1HDmy9UGNjmArqmGNyF5iISDfWrzRqvt+oPqUiWWPWEzgS2BlaGFHofmWql8ppUmpmJcBehHVRkz0J7N/O6a+ZWS/gPeBqd2/epF9mZvOBIsIarJe6+5sZCLvT1tbWsX5jfdsj7z/5BDZvhu22y11gEpsjjjgCgJkzZ8YciUj3VVzUg94lRaopFckWs62AWYSEtDX5mZQCFYSkcWmz/UuBQ1o5ZzGhFvVVwsiu7wHPmNmBSf1QK4H/At4G+gHnAC+Y2RfdvfmErpjZRGAiQElJSafeUCrmRyPvR7Y18r6qKmyVlBaEo446qv1CItJp5aVaalQki64AdmnjeFoDovJ+9L27V9K0MgDAS2Y2mjBQ6vmozEvAS4kCZvYiobb0bOBHLVzzduB2gD59+mR9/bn5qcxROjea9qu1UffSrUxKrMAlIllVXtZTU0KJZM9hhMRzGnBq9PwcQv7lwOR0LpbrFZ1WAA3A0Gb7hwLp9Ad9GdiptYPu3kBY+qrVMrm0YGU10M4cpVVVYVWmUaNyFJWISPenmlKRrEqs8nPhp3vcfw0cD3wOSGud85wmpe6+GXgdOLTZoUOBF9O41BhCs36LzMyAL7RVJpfmr6xhSL9e9C5po2K6qiqs4NSrV+4Ck9gccsghHHJIaz1WRCRTysuUlIpkUUO0XQmEXzSzIcD8aP/EdC4WR/P9TcA9ZvYK8AJwOrA1cBuAmd0N4O4TotfnAvMI85mWACcDxwInJC5oZpcB/wQ+BMoJTfZfIPRFjd38VTVs295KTnPnqum+gIwbNy7uEEQKQnlpTz5apuZ7kSxZSagt7U9o8d4G+COQmJKzjWmHPivnSam7TzezwcAlhMnzZwNHunsiq27efl0CXE94o7WE5PRb7v5YUpkBhD6iw4C1wJvA1939lay9kTQsWFnD/jsObrtQVRWo5qxg/OAHP4g7BJGCoJpSkayqJCSlOwB/B/4TSKwO48Ab6VwsloFO7n4rcGsrx8Y2e/1L4JftXO/HwI8zFV8mbaxrYMm6jW33J924MUwJpZH3IiIZ1b+smHW1dbg7oWeXiGTQVOAjoJQwEv8wYEh0bDlwbjoXy/vR913dpvpGvveVbdl39KDWC82fD+5qvi8gY8eOBWDWrFmxxiHS3ZWXFtPoUL25gb699F+eSEa5/wn406evzXYCvgHUAy/gviady+k3NMv6lxVz1bG7t11Ic5QWnFNOOSXuEEQKQnlZtKpTbZ2SUpFsc18HPNzR0/Ubmg+UlBYcJaUiuVFeWgzAuo11bN3CCogikj9yPU+ptGTu3DAV1PDhcUciOVJXV0ddnQZfiGRbeVmUlGoCfZG8p5rSfFBVBaNHQw/9jVAoDj00TNWrPqUi2fVpTWmt/ggUyXdKSvNBVZWa7gvMaaedFncIIgXh0z6lmhZKJO8pKc0Hc+fCV74SdxSSQyeffHLcIYgUhERN6VrVlIrkPbUXx23NmvBQTWlBqampoaamJu4wRLq9fqWJ0ffqUyqS71RTGjeNvC9IRx55JKA+pSLZ1rOoB31KitR8L9IFKCmN29y5YauJ8wvKGWecEXcIIgWjPFrVSUTym5LSuKmmtCCNGzcu7hBECkZ5abFqSkW6APUpjVtVFQwYEB5SMNauXcvatWvjDkOkIJSX9VSfUpEuQDWlcauqUtN9ATrmmGMA9SkVyYXy0mKWrNsYdxgi0g4lpXGbOxd23z3uKCTHfvSjH8UdgkjBKC8r5oNl6+MOQ0TaoaQ0To2NMG8eHHVU3JFIjh1//PFxhyBSMMpLe7K6uo5/fLgiY9fs0QO+NGogpcVFGbumSCaZ2STgAmA48C5wrrs/30rZ44HTgT2BUuA94Bp3fyRH4QJKSuO1ZAls2qTm+wK0YkX4z7GioiLmSES6v6H9S9mwqZ6T//ByRq974RG7cPqBO2T0miKZYGbjgCnAJOAf0Xamme3m7gtaOOVA4G/AJcAq4D+Bh8xsbGuJbDYoKY1TYjoojbwvOCeeeCKgPqUiuXDaAdvz5e0G0+iesWv+4O7XmL+yOmPXE8mw84Bp7j41en22mR0OnAFc1Lywu5/TbNcVZvYt4FhASWlB0HRQBesnP/lJ3CGIFIySnj3Ya9uBGb3miAFlLFmrwVOSf8ysBNgLuKHZoSeB/dO4VD9gdabiSkXBJ6WDBg2KrbZq27/9jdFmPD9/Po2LF8cSg8SjX79+gGpKRbqqk0bUsLlhrX6HJQ49zey1pNe3u/vtSa8rgCJgabPzlgKHpHIDMzsT2Aa4pzOBpqvgk9JVq1YxduzYeG4+bRpsvTVfP+yweO4vsVmyZAkAw4YNizkSEemIix96h5nvLubNk8bGHYoUnnp33ztbFzezE4DrgXHuPj9b92lJwSelsaqqUtN9gRo/fjygmlKRrmp4eSmra+rYWNegEfiSb1YADcDQZvuHAkvaOtHMTgTuBia4+6PZCa91SkrjVFUF3/hG3FFIDC688MK4QxCRThjavxSAZes2MWpw75ijEWni7pvN7HXgUOD+pEOHAg+0dp6ZnQTcBXzf3WdkN8qWKSmNy6ZNsHChakoL1OGHHx53CCLSCcPKQ1K6eG2tklLJRzcB95jZK8ALhDlItwZuAzCzuwHcfUL0ejyh/+j5wN/NLNG3bLO7r8pV0EpK47JgAbgrKS1QH3/8MQAjR46MORIR6YjhUU2pli+VfOTu081sMGHe0eHAbODIpD6io5qdcjohJ7w5eiQ8B4zNbrRNlJTGJTEdlCbOL0jf+973APUpFemqEs33S5WUSp5y91uBW1s5Nrat13FRUhoXTZxf0C655JK4QxCRTujXqye9S4pYsnZT3KGIdBtKSuNSVQUlJbD11nFHIjE45JCUpooTkTxlZgzrX8qSdbVxhyLSbfSIO4CCVVUFo0dDD30FhWju3LnMTdSWi0iXNKy8VKs6iWSQakrjMneumu4L2H/9138B6lMq0pUNKy/l5aqcDUwW6faUlMalqgr22SfuKCQmV1xxRdwhiEgnDetfytJ1G2lsdHr0sLjDEenylJTGYeVKWLUKdtop7kgkJgceeGDcIYhIJw3rX0p9o7OiehNb9SuNOxyRLk8dGuNQWRm2O+8cbxwSm8rKSioTPwci0iUNjSbQX6oR+CIZoZrSOCgpLXg//OEPAfUpFenKEhPoL15byx7b9I85GpGuT0lpHCorobg4jL6XgnTttdfGHYKIdFJiqVFNoC+SGUpK4/DBB7DjjtBTH3+h2n///eMOQUQ6aXDfXhT1MC01KpIh6lMah8pK+Nzn4o5CYjR79mxmz54ddxgi0glFPYyh/XqxWHOVimSEqupyraEBPvoIvv3tuCORGJ111lmA+pSKdHVDo2mhRKTzlJTm2rx5sHmzBjkVuOuvvz7uEEQkA4aVl/LB0vVxhyHSLSgpzbUPPghbJaUFbR8tnCDSLQzrX8rfP1gedxgi3YL6lOZaYjoo9SktaG+99RZvvfVW3GGISCcNKy+lenMD6zfWxR2KSJenmtJcq6yEgQOhoiLuSCRG5557LqA+pSJd3bD+TdNC9Sstjjkaka5NSWmuVVaGpnvTOsmF7Oabb447BBHJgMRcpUvWbmLHrfrFHI1I16akNNc++AAOOSTuKCRmY8aMiTsEEcmAYUmrOolI56hPaS5t2ACLFqk/qfDqq6/y6quvxh2GiHTSUK3qJJIxqinNJY28l8gFF1wAqE+pSFdXWlzEwN7FWtVJJAOUlOZSYuS9ktKC9+tf/zruEEQkQ4aWl7JEqzqJdJqS0lz64IMwwGnHHeOORGK2++67xx2CiGTIsP6lqikVyQD1Kc2lykoYPRpKS+OORGL24osv8uKLL8YdhohkwPD+pSxZuynuMES6PNWU5lJlpQY5CQAXX3wxoD6lIt3B0PJSVmzYxOb6Rkp6qq5HpKOUlOaKe2i+P+CAuCORPPC73/0u7hBEJEMSc5UuW7+RbQb2jjkaka5LSWmufPJJmBJKg5wE2Fk/ByLdRvKqTkpKRTpO7Qy5oumgJMlzzz3Hc889F3cYIpIBTRPoa7CTSGeopjRXEtNBqU+pAJdddhmgPqUi3UHTUqNKSkU6Q0lprlRWQu/eMGJE3JFIHrjjjjviDkFEMqR/WTGlxT20qpNIJykpzZXEyPse6jEhsP3228cdgohkiJkxrLyUJes0LZRIZyhDypUPPlB/UvnU008/zdNPPx13GCKSIWFVp9q4wxDp0lRTmgubNkFVFXz3u3FHInni6quvBuCQQw6JORIRyYTh/Ut5fcHquMMQ6dKUlObCnDnQ2KiaUvnUPffcE3cIIpJBQ/uXsnTtJtwdM4s7HJEuSUlpLiRG3isplcjIkSPjDkFEMmhYeSmbGxpZVb2ZwX17xR2OSJcUS59SM5tkZlVmttHMXjezr7VRdqyZeQuPXZqVO8HM3jOzTdH2uOy/kxQl5ijVdFASefzxx3n88cfjDkNEMmR4NFfpEo3AF+mwnCelZjYOmAJcC+wJvAjMNLNR7Zz6eWB40uPDpGvuB0wH/giMibb3m9mXM/4GOqKyEoYNg/LyuCORPDF58mQmT54cdxgikiFDy5tWdRKRjomj+f48YJq7T41en21mhwNnABe1cd4yd1/RyrFzgWfd/Zro9TVm9o1o/39kIuhOqaxU071s4b777os7BBHJIK3qJNJ5OU1KzawE2Au4odmhJ4H92zn9NTPrBbwHXO3uzyYd2w/4VbPyTwBndSLczKmshBNOiDsKySPDhg2LOwQRyaAhfXvRw+DNBWvYYUjfuMORmAzoXcwuw9Qq2lG5rimtAIqApc32LwVamxtnMaEW9VWgBPge8IyZHejuz0dlhrVyzfj/51+5MjxUUypJHn30UQCOOuqomCMRkUzoWdSDkYN6M+P1hcx4fWHc4UhMxu48hGmn7ht3GF1W3o++d/dKoDJp10tmNhq4AHi+pXPaY2YTgYkAJSUlnYywHf36wfPPw6j2usxKIbnxxhsBJaUi3cn//uArzF9ZHXcYEqMBZVnOKbq5XCelK4AGYGiz/UOBJWlc52VgfNLrJelc091vB24H6NOnj6dx3/SVlMABB2T1FtL1zJgxI+4QRCTDRgwoY8SAsrjDEOmycjr63t03A68DhzY7dChhFH6qxhCa9RNeysA1RXKmoqKCioqKuMMQERHJG3E0398E3GNmrwAvAKcDWwO3AZjZ3QDuPiF6fS4wD3iX0Kf0ZOBYIHnk0BTg72Z2IfBn4DjgG4CqKCUvPfjggwAcf/zxMUciIiKSH3KelLr7dDMbDFxCmG90NnCku8+PijTvfFkCXA9sA9QSktNvuftjSdd80czGA1cDVwJzgHHu/nJW34xIB91yyy2AklIREZEEc89ul8p816dPH6+uVsd0ya21a9cC0L9//5gjERGRrsTMaty9T9xxZEPej74X6Y6UjIqIiGwp58uMighMnz6d6dOnxx2GiIhI3lDzvZrvJQZjx44FYNasWbHGISIiXUt3br5XUqqkVGJQU1MDQO/evWOOREREupLunJSqT6lIDJSMioiIbEl9SkVicO+993LvvffGHYaIiEjeUPO9mu8lBupTKiIiHdGdm++VlCoplRjU1dUBUFxcHHMkIiLSlXTnpFR9SkVioGRURERkS+pTKhKDadOmMW3atLjDEBERyRtqvlfzvcRAfUpFRKQjunPzfcEnpWbWCNRm+LI9gfoMX1MyQ99NftP3k7/03eQ3fT/5K9PfTZm7d8uW7oJPSrPBzF5z973jjkM+S99NftP3k7/03eQ3fT/5S99N6rplpi0iIiIiXYuSUhERERGJnZLS7Lg97gCkVfpu8pu+n/yl7ya/6fvJX/puUqQ+pSIiIiISO9WUioiIiEjslJSKiIiISOyUlKbJzCaZWZWZbTSz183sa+2UPzAqt9HM5prZ6bmKtRCl8/2Y2fFm9qSZLTez9Wb2spkdnct4C0m6vztJ5x1gZvVmNjvbMRayDvzbVmJmV0bnbDKzBWb2o1zFW0g68N1818zeMrMaM1tiZvea2bBcxVtIzOzrZvaImS0yMzezU1I4Zw8ze87MaqPzfm5mloNw856S0jSY2ThgCnAtsCfwIjDTzEa1Un474LGo3J7AdcCvzOyE3ERcWNL9foADgb8B34rKPwY8lGqyJKnrwHeTOG8gcDfwTNaDLGAd/H7uAw4HJgI7A98B/pXlUAtOB/7f+SpwD3AX8HngWGA34I85Cbjw9AVmA+eQwkI8ZlYOPAUsBfaJzrsAOC+LMXYZGuiUBjN7GfiXu/8gad+HwAx3v6iF8r8Ajnf3nZL2/R74vLvvl4uYC0m6308r13gFeN7df5KlMAtSR78bM3sQeBsw4ER33z3rwRagDvzbdhhwP7CDu6/IXaSFpwPfzfnA2e6+bdK+U4FfuXvfXMRcqMxsA3CWu09ro8wZwC+Aoe5eG+27BDgD2MYLPClTTWmKzKwE2At4stmhJ4H9WzltvxbKPwHsbWbFmY2wsHXw+2lJP2B1puKSjn83ZjYJGApcnb3opIPfz7HAq8B5ZrbQzD40s1vMTElPBnXwu3kBGG5mR1lQAYwntARJ/PYjVHwk16o+AWwNjI4lojyipDR1FUARoco92VKgtb46w1op3zO6nmROR76fLZjZmcA2hKYvyZy0vxsz2wO4DDjZ3RuyG17B68jvzvbAAcAXgROAswhN+dOyE2LBSvu7cfeXCEnoH4HNwHJCS8P3sxempKG1vCBxrKApKRUBon6+1wPfdff5ccdTyMysFzAdON/dq+KOR1rUA3DC78vL7v4EITE9wcyGxhtaYTOz3YBfAVcRalkPJyQ7v4szLpFU9Iw7gC5kBdBAaE5MNhRY0so5S1opXx9dTzKnI98PAGZ2ImEwzQR3fzQ74RW0dL+b4cCuwJ1mdme0rwdgZlYPHOnuzZszpeM68ruzGFjk7muT9r0fbUfx2Zog6ZiOfDcXAa+4+/XR63+ZWTXwvJld7O4LsxOqpKi1vCBxrKCppjRF7r4ZeB04tNmhQwmjIVvyUivlX3P3usxGWNg6+P1gZicRmutPcfcZ2YuwcHXgu1kE7AGMSXrcBnwUPW/1+5T0dfB35wVg62Z9SD8XbdXSkCEd/G56ExLZZInX+j8/fi8BXzOz0qR9hwKfAPNiiSifuLseKT6AcYQ+OqcRanKmABuAbaPjdwN3J5XfDqgGbo7Knxadf0Lc76U7Pjrw/YwH6ghTcgxLegyK+710t0e6300L518OzI77fXTXRwd+d/oCHxNG4H8e+CphWpz7434v3e3Rge/mlOjftTMIfX+/ShiU9nrc76U7PqLfhcQfzzXAz6Pno6Lj1wHPJJXvT6gRvQ/YHTgeWAf8JO73kg8PNd+nwd2nm9lg4BJCE+NsQlNiomZgVLPyVWZ2JPA/hH8gPgF+5O4P5DDsgpHu9wOcTujCcnP0SHgOGJvdaAtLB74byaEO/Nu2wcwOIfRdfJUwY8WfgQtzF3Vh6MB3M83M+hH6+N4IrCXMx/yz3EVdUPYGnk16fUX0uIvwB8JwYIfEQXdfa2aHAr8BXiP87twI3JSjePOa5ikVERERkdipf4mIiIiIxE5JqYiIiIjETkmpiIiIiMROSamIiIiIxE5JqYiIiIjETkmpiIiIiMROSalInjOznczs12b2vpltMLP1ZvZvM5tqZl9JKjfPzNzM5sUYbiKWaVEsbmajk/YPNbM/mtliM2uIjt9sZqOTyk/LYlwDzOzy6HFsqnHnipmNTbp/e4/Lo3MSr2flOt72ZPN7Tee7ava5ZjQOEckcTZ4vksfM7FTgt0CvZod2jh5DgM8kV3lsCmGFmrgMAC6Lnt9FmPBdRETygJJSkTxlZgcBvye0aDhwDfA7YBmwLXAiTeuN5xV3P4Wwmklze0XbNcB27r4m6ZhlOax2tRF3ru4/i6TPwcxOAe6MXt4VxZdxZlbq7huzcW0RkVSp+V4kf11H0+/oLe5+qbsvdPfN7v6hu18H/KCtC5jZGDN70Mw+MrN1ZlZnZkuifXs3K7udmd1tZgvMbKOZrTGz2VEz6VZJ5X5gZq+Z2Soz22Rmi8zsKTP7flKZLZpWE82nwI5RkQHA6uj4KW0185rZl8zs/6L7bDazFWb2rJntGx3va2Z3mdk7ZrYyeo9rzOzvZjYu6TqXA1VJl/5+83u20e2gj5ldYWbvmlmtmdWY2Ztmdp6Z9Uwqt8X7MLMJ0WdYa6H7xffJIjM7yMz+Gd1vjpn91MySk9zLk+I7zsz+YGYrgNqkMrua2T1Jn/cyM5thZl9odq+Ufl6anXOSmf2rrc/DzL5mZo+Y2fKkn9f7mt+/jc9g6yjeDdHPw2+Bfq2UTfs9iEgWubseeuiRZw9gK0LtaOIxIoVz5kVl5yXtG9/sOsmPamDXpLLvtlF296jMd9ooMyPpWtOS9o8GxrZx3ilRmcTraUnXOQ6oa+28qMywNq7twISo3OVtlJnWUtzRvj7A622c+xjQIyqb/D5Wt1L+gDR+Dk5p6XNpViZxfEUrn9XJSWUvb1b+03LR8QOAmlbirgW+lubPS/LnsaS9zwM4GWhopdxGYGxrP2PRvjLg/RbO/aSlzzGV96CHHnrk7qGaUpH8NDrp+Tp3X9TB67wBfBMYTuiXWg6cER3rDfwQwMwGA7tF+28hJGKDgH2AS4G10bGvR9sNhD6tvQhdCU4CHm8tCHef5e4GzI92zXd3ix7TWjrHzMqAqTR1M/o5MBSoICTHc6P96wn9VEdH76kU2J+QXAGcF8VwObBd0i3uSorhlNZiB84FvhQ9f4LwWW5P+GwBjiAk/80NACYB/YFfJO3/Xhv36ozBwC+BgcBZKdzPgMMJn9ke0b6phMRuPqGrRS9gT2A54XP9DaT185JsKG18HmbWB/gVoXWgnvAHSTlwelSuF6H7SlsmALtEz/8JbEOonV/TvGAH34OIZJH6lIp0b0uA/wfcTEjaypod3znarib8xz2AkGStJ9Q4ve3uVyeVr4q2fYBLCDWI7wNPunum/xP/KiHRApjl7lclHZuR9LyGkKhOB3YlNNUm90/dmc75VtLzi9x9CYCZXUnTQKkjgf9tdt7r7v7bqOy9wM+i/dt2Mp7WLAV+7u4NZnYX8Ot27nejuz8RPZ9tZjvRlNBtS/hum9vDzIYR+jWn8vOSrL3P46vR9QAec/fEZ/s7MzsdGAN8zsx2dPePWrnHQUnPr0v8MWdmNxL6ZydL9WdeRHJENaUi+Wle0vNyM9u6g9f5E/BTQrLWPCElsc/dGwk1VguBnYD/Bu4F3on6ao6Myt8K3A8kyt9MqD1camYXdjDG1gxNev5eG+V+RqjB+zKhZq35gKnSTsYxJOn5gqTn85Oet9T/sDLpeXUG42nNHHdvSON+bzZ7nWofysFp/Lwka+/zaO1zhvY/609jS3q+sJXnQFo/8yKSI0pKRfKQuy8DXknadUFL5ZIH2bRwbCCh6R5CLdrngSKgxQEj7v4XYBShZvFo4EpC/77dCbWiuPtGdz+J0Mx5APBfwMuEptVrzWxEau8wJUuTnu/aRrnkpvNjgV5RV4GVLZT1DsSxLOn5qFaeJ5dJqOvkfdP16f3cPZX71TZ7nfwenk7q2vDpg9B39t3oHu3+vLQWHy1/Hq19zs1ft/RZJ6xIer5NK8+bgkj/PYhIFikpFclf/02okQT4UTRyemszK7Ywof7FhD6Aramn6T//emAdoZn7qpYKm9lKTGQ+AAACoUlEQVSvgIMJ/UUfBx4ANkWHR0VlTjCzs4ARwNuEWtO3E5eglf/8O+gFmhLLb5jZxWY2xMwGmtmxZpbo31qfdM4aoNjMLmXLWrOE5ER1p6gfY3v+mvT8GgsLAIwm9HFtqUyX5O4fAh9ELw82s3MtLDZQamZfNLOfA/clyqfy85KmFwlN6gBHmNnRFmZW+AGhXytAZRtN9wDPJj2/0MxGmNkOwE9aKpyF9yAinaCkVCRPufvThIFIdYTf1cuARcBmQvJwDWFQS2vnrweeiV6OAD4m1D7u1sopZwBPJd3jbcIgGAhN9BBqLH9FaE5fHz0mRscWA/9K4y22yd1rCVNeJZLOawi1ZKuAhwiDjYieJ8wiJBg/ooXBLe6+gTDiGsJgqA3R9EintBHKFLYc1LSE0Lc2MefqTEJ/1u5gImGUuwH/Q0gSa4G3gCvYsktFKj8vKXP3auBswh9ixcDDhJ+v26Mim2ga9NSau4F/R8/3IzTNf8SWXQOSZfQ9iEjnKCkVyWPu/ntCc/uthES0ltAfrxL4AzC5nUucTEiYVhNGE99L6ysqTQb+QUj86gkDiN4gJHhTojLPEAb0fERI/hoIyeh9wIFRIpkx7v4Qoa/ofYRpfeoJSelzNPUz/QVwLSGxqI2OHUTro6e/B/ydUHOcSgzVhFkHriQMhNlESNzeItTAHR31T+zy3P05QrJ9NyGhqyN83v8i/DFycVLxVH5e0r3/HwnTh/2FUKtdT/hD6k/Avh4WF2jr/FrgEOBBwu/JGsLiA63N55vx9yAiHWepdT0SEREREcke1ZSKiIiISOyUlIqIiIhI7JSUioiIiEjslJSKiIiISOyUlIqIiIhI7JSUioiIiEjslJSKiIiISOyUlIqIiIhI7JSUioiIiEjs/j8O3UQsMmkwrAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(figsize=(10,7))\n",
    "ax1.plot(class_thresh_arr, bal_acc_arr_orig)\n",
    "ax1.set_xlabel('Classification Thresholds', fontsize=16, fontweight='bold')\n",
    "ax1.set_ylabel('Balanced Accuracy', color='b', fontsize=16, fontweight='bold')\n",
    "ax1.xaxis.set_tick_params(labelsize=14)\n",
    "ax1.yaxis.set_tick_params(labelsize=14)\n",
    "\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(class_thresh_arr, np.abs(1.0-np.array(disp_imp_arr_orig)), color='r')\n",
    "ax2.set_ylabel('abs(1-disparate impact)', color='r', fontsize=16, fontweight='bold')\n",
    "ax2.axvline(best_class_thresh, color='k', linestyle=':')\n",
    "ax2.yaxis.set_tick_params(labelsize=14)\n",
    "ax2.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```abs(1-disparate impact)``` must be small (close to 0) for classifier predictions to be fair.\n",
    "\n",
    "However, for a classifier trained with original training data, at the best classification rate, this is quite high. This implies unfairness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(figsize=(10,7))\n",
    "ax1.plot(class_thresh_arr, bal_acc_arr_orig)\n",
    "ax1.set_xlabel('Classification Thresholds', fontsize=16, fontweight='bold')\n",
    "ax1.set_ylabel('Balanced Accuracy', color='b', fontsize=16, fontweight='bold')\n",
    "ax1.xaxis.set_tick_params(labelsize=14)\n",
    "ax1.yaxis.set_tick_params(labelsize=14)\n",
    "\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(class_thresh_arr, avg_odds_diff_arr_orig, color='r')\n",
    "ax2.set_ylabel('avg. odds diff.', color='r', fontsize=16, fontweight='bold')\n",
    "ax2.axvline(best_class_thresh, color='k', linestyle=':')\n",
    "ax2.yaxis.set_tick_params(labelsize=14)\n",
    "ax2.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```average odds difference = 0.5((FPR_unpriv-FPR_priv)+(TPR_unpriv-TPR_priv))``` must be close to zero for the classifier to be fair.\n",
    "\n",
    "However, for a classifier trained with original training data, at the best classification rate, this is quite high. This implies unfairness."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train classifier on transformed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "scale_transf = StandardScaler()\n",
    "X_train = scale_transf.fit_transform(dataset_transf_train.features)\n",
    "y_train = dataset_transf_train.labels.ravel()\n",
    "\n",
    "lmod = LogisticRegression()\n",
    "lmod.fit(X_train, y_train,\n",
    "        sample_weight=dataset_transf_train.instance_weights)\n",
    "y_train_pred = lmod.predict(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Obtain scores for transformed test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_transf_test_pred = dataset_orig_test.copy(deepcopy=True)\n",
    "X_test = scale_transf.fit_transform(dataset_transf_test_pred.features)\n",
    "y_test = dataset_transf_test_pred.labels\n",
    "dataset_transf_test_pred.scores = lmod.predict_proba(X_test)[:,pos_ind].reshape(-1,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predictions from the transformed test set at the optimal classification threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "#### Predictions from transformed testing data"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 13%|█▎        | 13/100 [00:00<00:00, 127.35it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification threshold used = 0.2674\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 41%|████      | 41/100 [00:00<00:00, 98.54it/s] "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Balanced accuracy = 0.7128\n",
      "Statistical parity difference = -0.0906\n",
      "Disparate impact = 0.7625\n",
      "Average odds difference = -0.0266\n",
      "Equal opportunity difference = -0.0518\n",
      "Theil index = 0.1294\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:01<00:00, 97.34it/s]\n"
     ]
    }
   ],
   "source": [
    "display(Markdown(\"#### Predictions from transformed testing data\"))\n",
    "bal_acc_arr_transf = []\n",
    "disp_imp_arr_transf = []\n",
    "avg_odds_diff_arr_transf = []\n",
    "\n",
    "print(\"Classification threshold used = %.4f\" % best_class_thresh)\n",
    "for thresh in tqdm(class_thresh_arr):\n",
    "    \n",
    "    if thresh == best_class_thresh:\n",
    "        disp = True\n",
    "    else:\n",
    "        disp = False\n",
    "    \n",
    "    fav_inds = dataset_transf_test_pred.scores > thresh\n",
    "    dataset_transf_test_pred.labels[fav_inds] = dataset_transf_test_pred.favorable_label\n",
    "    dataset_transf_test_pred.labels[~fav_inds] = dataset_transf_test_pred.unfavorable_label\n",
    "    \n",
    "    metric_test_aft = compute_metrics(dataset_orig_test, dataset_transf_test_pred, \n",
    "                                      unprivileged_groups, privileged_groups,\n",
    "                                      disp = disp)\n",
    "\n",
    "    bal_acc_arr_transf.append(metric_test_aft[\"Balanced accuracy\"])\n",
    "    avg_odds_diff_arr_transf.append(metric_test_aft[\"Average odds difference\"])\n",
    "    disp_imp_arr_transf.append(metric_test_aft[\"Disparate impact\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Display results for all thresholds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KiEsNmpRaa/3Al8CYkI/GAAuOveMIY8wpwCCO7boHWFhFm0uttVo9vBHZklNIl8y0mF9r8+233+btt9/27PkXDGxPQpzhbXXhS31TUioiLnnRff8YcLUx5jpjTD9jzONAB+BJAGPMC8aYFyq57wZgrbV2biWfPQl0NMZMC7R5HXA18Kd6+Q7EM1t8hTE/yQmgZcuWtPRwjF1mehJn9mnDu8t2UF6uLnypRxpTKiIuNXhSaq19DZgC3AUsw1lTdKy1dnPgki6B12HGmObAeODpKtrcCIwFzgy0+TvgV1qjtHEpL7ds8RXG/HhSgNdee43XXnvN0xguHtyBHXlFx2xvKhJRqpSKiEueDKi01k4Hplfx2ahKzh0AmtXQ5mfASZGIT6LTngPFFJeW06V1bM+8B/jb3/4GwLhx4zyLYXS/bFIT43n7mx0Mi/HNCCSKKSkVEZdibfa9NGHBmfddG0GldNasWcyaNcvTGNKTExjTP5tZy3fiLy33NBZpxIJJafPm3sYhIlFPSanEjC2BNUobw5jStLQ00tK8/z4uHtyB/YUlfL52r9ehSGOVlwfp6aCVTkSkBvotITFji6+Q+DhDh1apXodSZy+99BIAEydO9DSOM3q3oVVaIm8v28HZ/UJXVYOycsvB4lLnVVTKweISSsqOnRjVs00z2jRPboiQJdbk56vrXkRcUVIqMWNzTiEdWqWQGB/7Bf6nn3bm7HmdlCYlxDF2YHv+/dV2VmzPY/3eg6zYnsd3O/JZuTOf3EJ3K6oN6NiC/9x8Rj1HKzFJSamIuKSkVGLGZl8hXWN8e9Ggjz76yOsQDrtkcEdeXrSFH/7ffACS4uM4rn1zzj2+He1aptAsOYHmKQk0S06kWUoCifFHrxH7yco9PD1/Ixv3FdA9q3H8/yMRpKRUxBPGmMnAbUB74DtgirX28yquHQV8WslH/ay1q+otyBBKSiVmbMkp4PyB9bsvfENJTEz0OoTDhnbL4IFLBpCaGM/xHVrQq22zsKrRXVun8/T8jby/YieTR/Wqx0glJuXlaY1SkQZmjBkHPA5MBuYHju8bY/pba7dUc+vxQMV1Aht0wkHs94NKk5BfVEJuYUmjmHkPMHPmTGbOnOl1GAAYY5h0aleuGNKJfu1bhD08omOrVAZ3bsWs5TvrKUKJaaqUinjhN8BMa+1T1tqV1tqbgZ3AL2u4b4+1dleFV1n9h3qEklKJWv7Schas28dD733PpX/9AoBujaR7OJqS0ki4YGB7VmzPZ0tOodehSLRRUirSoIwxScAQ4MOQjz4Ehtdw+1JjzE5jzMfGmLPqJcBqqPteokZRSRnLt+exbMt+Fm/ysWDdPgr8ZSTFxzGsRyYTT+3KqL5tvA4zIubOnet1CBF13oB2PDRrJe+v2MnPR/b0OhyJJnl5SkpFGlYWEA/sDjm/GxhdxT3BKuoSIAmYBHxsjBlZ1TjU+tDkk9LMzMxGlyBEq4PFpRQUlx5zvqzcUugvo6ikHIuz3NDJSXGMOimBFinJpCcnEGcOQclmFs7ffMz9Eh3uGmIxe1Yxd+5Wr0ORaFFezsgDB9icm8sm/Z4ViZQEY8zSCu9nWGtn1KVBa+1qYHWFUwuNMd1wJkopKW0oPp+PUaNGeR1Go2WtZe6avTw+Zy3LthYQZyDOHD17OzUpnhM6ZTC4cysGd3aOjX3Ny6eeegqA66+/3uNIImfl3PU8MnsV8//fKXTKaBxjf6WODhwAa+l2wgl00+9ZkUgptdaeXM3n+4AyIHTx6WxgVxjPWQSMDzO2OmnySanUD2stc1fvZdrHa/lm6346tkrloUsHcMWQTiQnxHsdnudee+01oHElpWMHtuOR2auYvWIX153Rw+twJBpo33uRBmet9RtjvgTGAP+q8NEY4I0wmhqM063fYJSUSsQVlZRx08tfMWflHjplpPKHywZy+UmdSErQvLqgOXPmeB1CxHVtnc7xHVowa/lOJaXiyMtzjloSSqShPQa8aIxZDHwB/ALoADwJYIx5AcBa+9PA+ynAJpz1TJOAicAlwOUNGbSSUomoopIyfv7il3y2Zi+/G9uPq0/v1ih2YBJ3xg5sz6MfrGZn3iHat4z97WCljlQpFfGEtfY1Y0xr4C6cxfNXAGOttcGJGV1CbkkCHgU6AYdwktMLrLWzGihkQEtCSQRVTEinXjaQ68/soYS0CtOnT2f69OlehxFx5w9oB8D7y8MZtiSNlpJSEc9Ya6dba7tZa5OttUOstfMqfDbKWjuqwvs/Wmt7W2tTrbWZ1tozGjohBSWlEiGhCen4U0L/CJOK3n33Xd59912vw4i4Hm2acVy75ry/QgvpC0pKRSQs6r6XOisqKeOGF7/k87V7eeTygYwbqoS0Ju+//77XIdSb8we0Z9rHa9idX0R2ixSvwxEvaUypiIRBlVKps9te/9ZJSC87QQmpMHZgO6yFD75TF36Tp0qpiIRBSanUyd4Dxbz37Q6uP6MHVw7t7HU4MePxxx/n8ccf9zqMetE7uzm92zbj319vp6zceh2OeCmYlDZv7m0cIhITlJRKncz+bhflFi47qaPXocSUjz/+mI8//tjrMOrN1ad34+st+/nVK1/jLy33OhzxSl4epKdDvNYmFpGaaUyp1Ml73+6gZ5t0+marEhKOd955x+sQ6tWEYV0pLC7joVkrOVRSxvQJJ5GSqMSkycnP13hSEXFNlVKptT0Hili00ccFJ3TAhGwdKnL9mT146NIBfLp6D9c8t4SC4lKvQ5KGlp+v8aQi4poqpVJrs1fswlr44QntvQ4l5vzpT38C4NZbb/U4kvo1YVhX0pLiufVf3zLxmUXMvPoUTBys3JHP9zvz+X5HPrmFJfzxihPITE/yOlyJNCWlIhIGJaVSa+99u5PebZvRR133YVu4cKHXITSYS0/sRGpiAje/8hXDp35Mgb/s8GeZ6UkcKCrh/73xLTMmDVHFvbHJy1NSKiKuKSmVWtmTX8TiTT5uObu316HEpDfeeMPrEBrUeQPa8fw1p/D6V9vo2aYZ/du3oH+HFrRtnswz8zfy4HsreXXJVn6sTRcal/x86KhJkCLijpJSqZX3A133FwxU1724M7xXFsN7ZR1z/mend+ezNXu5/93vOaV7Jj3bNPMgOqkX6r4XkTBoopPUynvf7qRPdjN6q+u+VqZOncrUqVO9DiMqxMUZ/vSjQaQkxnHLq1pCqlFRUioiYVBSKmHblVfEks0+LhjYwetQYtayZctYtmyZ12FEjewWKUy9/ARWbM/nsY/WeB2OREJ5ORw4oKRURFxT972E7f0VO52u+xPaeR1KzHr11Ve9DiHqnHt8O358Shf+Pm89Z/bJYnjPY7v6JYYcPAjWap1SEXFNSamE7b1vd3Jcu+b0aquue4msu3/Yj0Ubcpjy6jJG9mlDgb+UguIyCopLKSkr576LBzC4cyuvwxQ3tO+9iIRJ3fcSlp15h1i6OVcTnOrogQce4IEHHvA6jKiTlpTAEz8+kfTkBL5Yt481uw+y/1AJSQlxrNtzkJlfbPQ6RHErL885KikVaZyMScaYCRjzIsaswZh8jDmAMWsx5mWMuRpjUsNpUpVSCcus5bsAGKsF8+tk9erVXocQtQZ0bMmnt4465vwdby7n7WXbKfSXkpakX11RT5VSkcbJmHTgf4CbgGDXVcVFpnsCPYBxwP9izF+AqVhbUFPT+s0urvlLy/n319vo176Flu2po5deesnrEGLOxYM78MriLXz0/W4uHqy1L6NeMCnVmFKRxmYd0JYjieg+4NvAESALOCFwbAncCVwL1Dg7WkmpuLLVV8hNr3zNiu35TL1soNfhSBN0SrdM2rdM4Z1lO5SUxgJVSkUaq2xgCzATeBVrV1V6lTHHAeOBa4BObhpWUio1mvP9bn77r28oL7f8bcJJnK/xpHX2+9//HoD777/f40hiR1yc4aJBHXhm/kZyC/xkpCd5HZJUR2NKRRqra4EXsba02qucZPVejHkQmOSmYU10kiqVlJXzh1krue6FpXTKSOU/vxqhhDRCtm7dytatW70OI+ZcNLgDpeWWWSt2eh2K1ESVUpHGydrnakxIj76+FGufc3OpKqVSqYPFpfzsuSUs3uRjwrAu3P3D/qQkxnsdVqPx3HOufj4lRP/2LejVthlvf72DCcO6eh2OVCeYlDbX0nEijZYxnwAWa8+u5DOnS9Ba112CqpTKMUrLyvnVK1/z5ZZcpo0bzEOXDlRCKlHBGMPFgzqweJOP7fsPeR2OVCc/H5o1g3j97hBpxEYFXpW5F/h9OI0pKZVjPPjeSj5ZtYf7LjqeS07UhJL6cMcdd3DHHXd4HUZMCk5yevebHR5HItXKy1PXvUhTZUxlS0XVSN33cpSZX2xk5oJNXDeiOxNPVfdofcnJyfE6hJjVpXUaJ3ZpxdvLdvCLkT29Dkeqkp+v5aBEGiNjrgKuCjn3SchVwQQiN5ymlZTKYZ+s2s39//meMf2zuWNsP6/DadRmzJjhdQgx7eJBHbj33e9Zs/sAfbI1ZjEq5eerUirSOHXD6bK3gfcGGBlyTbBCujCchpWUCgDf7cjjppe/5vgOLXl8/GDi48KquIs0qAtO6MAD763k7WXbue3c4wAoK7e8vWw70+asZYuvsNZtt05P4uYf9GLCqV1JjNcIp1pTUirS2BmOTkwrygH+C9wcVoPW2pqvasTS09NtQUGNO181avsL/Zw37XOMgbduPJ3sFileh9To3XrrrQD86U9/8jiS2PXTZxezYe9B5t12Fh+v2sOjH6xize6DHN+hBWf3yw5vIFMFizf6WLghh+5Z6dx+/nGc0z8bY/RHWtj693der7/udSQijYoxptBam+51HAAYU44z+z4iMxpVKRVeW7KVXflFvK2EtMEcOqSZ43V18aAO/PZf33De4/NYs/sg3bPS+ctPTmTsgPbE1aHSb63lk1V7eHjWSn7+4pec0j2T34zpQ9vmyTXemxAXR+fMVCWxoDGlIk3DNZFsTJXSJl4pLS+3nPXnuWQ3T+GfvzjN63BEXDtQVMLwP3xCWnI8U0b34YohnSLa3V5aVs4rS7Yy7aM15BT4Xd/34CUDNEkQnK77a6+F//1fryMRaVSirFLaHegM7MXalRXO9wPaAFuxdqPb5lQpbeLmr9vH5pxCfjOmj9ehiISleUoin9w6iuYpCfWyjm5CfByTTu3KJYM7MG/NPkrLy2u85+nPN/K3uesZN7Rz0x6PWlYGBw5oTKlI4/dX4FzgZ8DKCudPBmYCs4EL3DampLSJ+8eizWSmJ3HegHZeh9KkTJkyBYBp06Z5HElsa+OiS72umqckcsEJ7rbXbZGSyDUzl/Dvr7dz5cmd6zmyKHbwoHNUUirS2J0UOL4fcn42zuSnkwhDE/5TXnblFTFn5R5+dHInkhO064pIXY3q24bjO7TgybnrKStvwkOjgluMakypSGOXETgWhZwPjnnKDKcxJaVN2KtLtlBWbplwisa/NbRp06apStoIGWO48axebNhXwPsrdnodjneCSakqpSKNXXBx/B+FnL885HNXlJQ2UaVl5by6eCtn9mlDl9ZpXocj0mice3w7erRJ56+frqfJTiTNy3OOSkpFGrv/4nTTT8eYZzDmtxjzNPA3nDVM/xtOY0pKm6iPV+1hV34RE4d18TqUJunGG2/kxhtv9DoMqQfxcYbJo3qxcmc+n6za43U43lClVKSpeBwn+UwArgb+iLNMVGLgfFjLbygpbaL+sWgL7Vum8IPj2nodSpOUmppKamqq12FIPbl4cAc6ZaTyl0/XRa5aunMnvPACTJoEF1wAH3wA0VqJ1ZhSkabB2k+BKUAJTsU0+PIDv8baz8JpztXse2MYZi2LwgxVotTmnALmrdnLr0f3IaEpL1vjIe3k1Lglxsfx85E9ufutFSzckMPwnlm1a2j9evjrX+Gjj2DFCudcVhYkJcF558Hw4XDvvTB6NETTgv2qlIo0Hdb+H8a8CZwHZAO7gdlYuz3cptwuCbXQGJYDTwMvWRvewFWJLi8v3kJ8nGHc0Ca8ZI1IPfvRkE488fFa/vrputonpX/4Azz3HPzgB06FdMwYGDQISkqc8w89BOecAyNGwK23Qps2kf0mauvbb52jklKwslhLAAAgAElEQVSRpsFJQJ+pazOudnQyBmdvU0cx8G/gaWv5tK4BeK2p7ehUXFrGaX/4hFO6ZfLkpCFeh9Nk3XDDDQDMmDHD40ikPs2Yt56HZ63i3gv7c1LXDHq0aUaz5DCWh54wARYvhrVrK/+8uBiefhoefhh27IhM0JGSluZUTOO13JxIJEXVjk4AxmQCE4G+QOi4NIu117ptyu1vx8eAK4FOQAowHhhvDBtwMuOZ1rLL7UPFO+99uxNfgZ8Jp2qCk5dat27tdQjSACYM68o/Fm3h3ne/P3yubfNkerZpxs9H9mBU3xrGdPv9Tld9VZKT4cYbne08FyyA0tIIRR4BnTsrIRVp7IzpCcwHKvtlZnAKmq6TUleV0iPPZgTwY5z1p4IBWKAMeBt4yFqWuW4wCjSlSqm1lh/+33z8peV8+OszMdE0Bk2kkfKXlrPFV8C6PQVs2HeQ9XsK+OC7XZzRO4u/Tayht+Lii2HzZlgWU79WRaQeRVWl1JgXgQnVXGGx1vVfp2HNcrGW+dZyIzAUqDijKgG4DFhkDBeH06Y0nP9u8PHdjnyuHdFdCalIA0lKiKNX2+acN6Adk0f14s9XDqJ/+xb4Cvw131xTpVRExFsjcYqTDwXeW+AinPVJ1wDnh9NYWEmpMYwxhjeAdcCZwdPA18AGnHWpHqridvHYM/M30Do9iUtO7Oh1KE3eNddcwzXXXON1GOKRjPREcgtdJKUlJUpKRSSaZQeOfz58xtr/4PSq98GZke+aq6TUGG4zhrXAbOASjiyK+hYw0lqGAIOB/EAQEmU27D3InJV7mHhqV1ISNc7La507d6ZzZ61+0FRlpie7r5QmJtZ/QCIitRP8RZaPMxEejOkEHAicr65r/xhuJzo9gpOEmsCDnwWesJZNwQuspcAYdgG9wwlAGsazX2wkKSGOSadpn/tocP/993sdgngoMz2R3MISysstcXHVDKXx+7UAvYhEs31AFyAT2Ab0AGYRTFAhOZzGwum+3wj8GuhkLb+pmJBW8INAQBJFcgv8vP7lNi47sSNZzcL69yEi9SAzPZmycsuBohpmy2tMqYhEt+8Cx77ARzjFy+OBk3CKmfPDacxtpfRS4B1rqXaqvrVE2UJ5AvCPRZspKinnZyO6ex2KBEycOBGAl156yeNIxAuZ6U6XfE5BMS3Tqume15hSEYlujwGfA4XAfTgTn/oFPlsJ/CqcxtwmpXOBzsZQaC37gieNIQtIA/KsJS+cB0vDKC4t4/mFmxnZpw19spt7HY4E9O3b1+sQxEMZaU6iWeNkJ40pFZFoZu0nwCeH3xszADgBKAVWYW1ZOM25TUqfxZng9GvgiQrnxwOP4+zwdEU4D5aG8e43O9l7oJjrrlSVNJrcfffdXocgHmqd7gyjyTnoIilVpVREop0xrXFWZcrCGWc6L9yEFNyPKR0WOL4Rcv5NnPEDw5CoY63l6c830De7OSN61XLvbRGJuIxA932NlVJ134tILRljJhtjNhpjiowxXxpjznB53whjTKkxZoXLB92LM8npdeDJwHEbxtwTbsxuk9I2geP+kPN5IZ9LFPl09R5W7TrAtWdosfxoM378eMaPH+91GOKRzHQn0fQVlFR/obrvRaQWjDHjcHqyHwZOBBYA7xtjqt1j3BiTAbwAfOzyQbcBv8eZZW8qvJKB32PMb8OJ221SGlxv6pyQ88H3B8N5qNS/FdvzuOWVZfRsk85Fgzp4HY6EGDx4MIMHD/Y6DPFIWlICKYlx+AqKq79Q3fciUju/AWZaa5+y1q601t4M7AR+WcN9zwDPAwtdPufGwPEQ8DIwNXA8hJOc3hxO0G7HlH4FjAaeNYbjcWZU9cP5pi3wZTgPNcZMBm4D2uMsJzDFWvt5NdcnAXcBk4AOwG7gT9baJwKfXw08V8mtqdbaonBiawzW7TnIT59dTIvURF68dpgWy49Ct99+u9chiMcy05LcVUqVlIpIGAI50xDgTyEffQgMr+a+yTg7ND0IuJ34kI2TB16MtXMqNDYG+ABo6zpw3CelT+IkpS1wpvwffmwgmCfdPrBCSXkyzvpVk3FKyv2ttVuquO1VoBNwA7AW53+E1JBrCoGeFU+4SUgzMzOZO3eu2/CjXklZOev3FnB9L+jZJp01yxaxxuugROQYP+t5iIT47cydm1vlNWf6/WzduZONjeh3lIjUWYIxZmmF9zOstTMqvM8C4nEKeBXtxsnljmGMGQjcA5xqrS0LY8jfSmAQzl73FQUrre7GpQa4Skqt5U1jeAynMhrqz9by7zCeebikHHh/szHmPJyS8h2hFxtjzgHOBnpaa4PLUW2qNExrd4URBwA+n49Ro0aFe1tU2nOgiCufXIivIInXfn4a/dq38DokqcLll18OwBtvhM4dlKbimWcWkV9UytuXn175BeXlUFZG11696NpIfkeJSESUWmtPjlRjxphk4DXgVmvtxjBvvwt4B6fA+McK5ycDJcCd4TTmtlKKtdxqDK8BF+FUKnfjLKi/xG0btSwpXwIsAX5jjPkpzjiF94E7rbUVx7KmGmM24/x1sAy421r7tdvYYt3+Qj8/fWYxew4U89J1w5SQRrnTTjvN6xDEY5npSWzOKaz6gpJA176670UkPPuAMpxcraJsoLLiXXucIZnPGWOCQyHjAGOMKQXGWms/rOJZt+FMev8DxtwEbMXp2e4E7AXuxJhgYmqx9uzqAnedlAIEElDXSWglwi4p42xbOgJnH9XLgVbA/+GMLQ2ujboa+BnwDdAcuAX4whgzyFq7NrRBY8wNOEMBSGokv/D/Pm8Da/cc5PlrTuGkLhlehyM1uPXWW70OQTyWmZ6Er6CaJaGUlIpILVhr/caYL4ExwL8qfDSGY5f2BNgODAw5Nzlw/aVU3jsdNBIO7/bZMfAKahP4HI4M96yW66TUGBKAsTj7m4aO58Ra7nfbVpjicL6Rn1hr85xYzE3AB8aYbGvtbmvtQirMFDPGLMCplt5MJVtcBcZezABIT0+v8X+kWLA99xCdM1IZ0VvrkYrEgsy0JA4Wl1JcWkZyQiWTEf2BhFVJqYiE7zHgRWPMYuAL4Bc4xbwnAYwxLwBYa39qrS0hZOynMWYPUGytdTMmNGJrTrpKSo2hLc5Wo9XtjegmKQ23pAzOEgbbgwlpwMrAsQvHVl0JDNJdCvR2EVOjkFvoJyNd//GKFRdddBEA77zzjseRiFcymwW2Gi0ooV3LapJSrVMqImGy1r5mnF2W7sLpnl+B0w2/OXBJteuVhvEgt0uLuuK2sfuA4zh6YdSKL1estX6c5aPGhHw0Bmdh18p8AXQwxjSrcK5P4Li5kusxzrSxE3AS2ibBV+AnM01Jaaw4++yzOfvsaofWSCMX/HmtsgtflVIRqQNr7XRrbTdrbbK1doi1dl6Fz0ZZa0dVc++91toBDRJoBW6778/B6UKfCVwT+PoWnO5xi7NYqluuS8qB61/GWS/rOeNsZdUKZ0mp1621ewL33IOzHMFanGWrfoWTlNa0SGyjkVvg1+SmGHLLLbd4HYJ4LNizUeVWoxpTKiKxwJg4nO3mu+Ds5HQ0a19w25TbpDQ4cPV2nKQUa/mLMXwKLMeZZeVKuCVla+1BY8xonMlNS4Bc4K1ALEGtcMaItsOZBfY1cKa1drHbuGKZtZacAv/hrQtFJPq1Dvy85tRUKVX3vYhEK2P6AW8Tsk58BRZn21JX3CalZUAikIOz7lSCMbThSPf5DTg7ALhirZ0OTK/is1GVnFvNsVucVvz818Cv3T6/sTlUUkZxabmS0hhy/vnnA/D+++97HIl45XClVN33IhK7pgO9ItWY26Q0B6da2hJnQlIn4B9AcMckrUHkoeCYNI0pjR0XXnih1yGIx1qlOhVQjSkVkRg2BKca+hYwG6hmnbuauU1KV+MkpT2BecAEnF2WCATzVV2CkLrJDeyfrdn3sWPy5MlehyAeS4iPo1VaYtVJqcaUikj0242znvzVWHugro25nX3/FM6YzRScmfh7OTLzfh8wpa6BSO35AhMlMtM19kwklmSmJR3++T2GxpSKSPR7GCcXvBVnu9I6cVUptZZ/Av8MvjeG3sBZQCnwhbXsr2sgUnu+gmIAMtPr/O9BGsjo0c4GZnPmzPE4EvFSZnoSvoPqvheRGGXtcxhzMc7k9f+Hs+h+acUrsLaqSVDHqDEpNYZk4PvA2wusZZW15OPMtpIo4At032tMaewYN26c1yFIFMhIT2Krr7DyD9V9LyLRzpg7gItwhnImcfQ2o662Fq2oxqTUWoqNoTXOnvIbwmlcGkZugZ/4OEPzFNe7xorHrr/+eq9DkCiQmZbEN1ur6GhSpVREot/NgaMJOdaK2zGlwT7GQXV5mNQPX6GfjLRE4uIitv2siDSAzGZJ5Bb6sbaSYoLGlIpI9GuGUw29FEjD2riQVyV7KFfNbVI6DfABrxjDOGPoawxdKr7C+x4kknwHtXB+rBk1ahSjRo3yOgzxWGZaEiVllgPFpcd+qEqpiES/dwLHJVhbVO2VLrjt752Hkwln4mz7GcqG0ZZEmFMp1X+4YsnVV1/tdQgSBYJ/TPoO+mmRElIR1ZhSEYl+r+NsbvQ+xjwObOLoiU5g7Ty3jYWTSKpvOErlFvjp1baZ12FIGJSUClRISgv9dCP96A/VfS8i0e9NnMJka5zlQ0OFVbR0e+HzbhuUhpdb6NfC+TGmJFAFS1TC0aRVu9Wouu9FJDZErGjpdp3SayL1QIms8nJLbmGJloOKMWPGjAFg7ty53gYinmodSEpzKktK1X0vItEvokVLjQONcflFJZSVW010ijHXXXed1yFIFFClVERimrURLVq6SkqN4dkaLrHWcm0E4pEwBffNVlIaWyZOnOh1CBIF0pPiSYqPO/xzfBSNKRWRJsZtpfRqql6VP7hiv5JSD+QG9s3WmNLYUljo7OKTlpbmcSTiJWOMs9VoVUlpfDzEuV25T0SkARjzLM72odcGvq6Oc51Lmn0f47TFaGwaO3YsoDGl4vxBGfzj8iglJeq6F5FodDVQjlOMvJqatxKNeFLavZL7egB3AycCP3T7QIksX0Ex4OwMI7Hjl7/8pdchSJRonZ5U+UQnv19JqYhEK1PF16FqSliP4nb2/eZKTq83hoXAPuCXwGfhPFgiQ5XS2DRu3DivQxAvffUVZGVBly5kpCexLbfw2Gv8fo0nFZFo1L2Kr+usrrPvE3Cy4PMiEIvUQm6hn5TEOFKTwtpeVjyWl5cHQMuWLT2ORDxx4YVw7rnw7LNkpiVWPaZUlVIRiTbWbq706wioy+z7FOB0IBnIi2RQ4p6vwK8qaQy6+OKLAY0pbZL27oUdO+DAAQAy05PJLyqlpKycxPgKk5o0plREmpi6zr4PjiOYFZFoJGy+Ar/Gk8agX/3qV16HIF5ZscI5FgfGg6c7XfS5hX7aNk85cp2670Wkianr7Pti4BVgSmTCkXD5CvxkqFIacy677DKvQxCvBJPSoiLAqZSC87N8TFKqSqmINCG1nX0PUGwtuyIZjIQvt9BP19Za6zLW7Nu3D4CsrCyPI5EGF1IpzQhUSo8ZV6ruexFpYuoy+16igCqlsemKK64ANKa0SVq+3Dke7r4PbjVacvR1qpSKSBPjdqLTecApwNfW8m6F8xcBg4HF1jK7fkKUqvhLyzlQVKotRmPQb3/7W69DEC9YW8mYUufnN7jm8GEaUyoiscaYVKw9VNvb3Xbf/x4YBpwfcv4gcC+wEJSUNrT9hdr3PlZdeOGFXocgXti69fCs+8Pd92nBpLSSSmlyckNGJyISPmN6A48CY3BWZErAmGlAC+DPWPud26bcbqp8XOC4MOT84sCxn9sHSuT4lJTGrF27drFrl4ZkNznBrvtu3Q4npYnxcTRPSTi2UqoxpSIS7YzpipMbXgikcmRSfAlwFfCTcJpzm5QGZ9I0CznfPORzaUDBiREaUxp7xo8fz/jx470OQxpasOt+yJDDSSk4W436CjWmVERizr1AJk4SWtHrOAnq6HAac9t9vxPoAvwOuKnC+TsDxx3hPFQiI5iUqlIae26//XavQxAvrFgBnTtD27ZHJaUZ6Unkhs6+15hSEYl+5+CsY38u8GmF84FuIbqG05jbpHQOcC3wS2M4B1gN9AV6BoKZE85DJTJylZTGrPPO0868TdLy5TBggDNWNKRSun1/0dHXqvteRKJfm8BxQcj5YDd+RjiNue2+n4ozqQmcRHRs4GiAgsDn0sCCEyNapamaEmu2bt3K1q1bvQ5DGlJpKaxcWWlSmpFWRaVUSamIRDdf4Ngt5PxFgWNOOI25SkqtZT1OiXYVTiIafH0PnGMtG8J5qERGbqGfFikJR++XLTFh0qRJTJo0yeswpCGtW+ckmgMHOkmp3+8sEYXT2+Er8GNthd2c1X0vItEvOAH+5cNnjPk78CxOT/r8cBpzvc2otfwXON4YegLZwO5Asioe8RX41XUfo+666y6vQxA3/H7Ytg127z6cQFYrMRFOOgni44/9LDjzfsAAZ2moYPvJyWSmJ+EvK6fAX0az5IQjn6lSKiLR7RHgh8BJOEkowHU4hcsy4M/hNOY6KQ0KJKJKRqOAr8BPhpLSmDR6dFgTEiUcTz0F8+bV/v5Dh5ykccsWqM2yXdOnwy9/eez5FSsgLg6OOw4++cQ5V1QEycmHf45zC/xHklKNKRWRaGftfzFmAjAdZxZ+UC5wI9YuCqc5tzs6/QMYD9xrLQ9UOH83znIAL1uL+iIbmK/AT4dWKV6HIbWwYYMz4qVHjx4eR9II3Xmnk9C1bl27+5OToVMnuOACZ6Z8ly7Qrl3l1c9Qv/kNPP981Ulp796QmnpkUfzAuNLWgaQ0p8BP58zACnuqlIpILLD2nxjzLnA60BbYAyzA2sJwm3JbKR0eOL4Ucv5F4D5gRLgPlrrLLfRzfIcWXochtfCzn/0MgLlz53obSGOTnw/79sEjj8D//E/DP/+qq5znrlsHvXod/dny5XDCCc7XIUlpxUrpYRpTKiLRzhhn7Ki11xK6EpMxPwXA2hfcNud2hkz7wDG0L2t34NjO7QMlMqy1GlMaw+677z7uu+8+r8NofNYHRhb17OnN83/8YzAGXn756POHDjmJ6oABzvuQpDTz8FajgaS0rAzKy1UpFZFod3XgVZmZOBOeXHOblAYX0Dst5PxpIZ9LAyn0l1FcWq4xpTFq5MiRjBw50uswGh+vk9JOnWDkSHjppaMnRq1c6bwfONB5nxIYdhNMSpuFJKUlgc1RlJSKSCwyJrjTp6n2uhBuu++X44wVmGkMdwIrcfa7fwhnttXyau6VeqDdnGLb6tWrAejbt6/HkTQyXielABMmwPXXw9KlMHSoc67izHs4plLaPDmBhDiDrzCQlPoDR3Xfi0i0MeZi4OKQc6EV0d6BY344TbtNSmfiJKUdgecrhoGTlM4M56FSd7mB/3hlat/7mPTzn/8c0JjSiFu/Htq0gebNvYvhiivgxhvhH/84kpSuWOEkosFkOSQpNcaQkZ6E72BIUqpKqYhEn8E4XfbB7iADXFXJdRZYFk7DbhfPfwZ4g6MXzg+WZF+3NrwxA1J3wUqpuu9j08MPP8zDDz/sdRjRqbQU3n4bcnPDv3f9em+rpACtWjkz91991flewElK+/eHhEAdICQpBWcG/uFKqbrvRST6BQuTlmPzw+AGS1PCadD1VkDW8iOcZaH+gTPD6h/AOGsZF84DJTLUfR/bhg8fzvDhw2u+sKkpLXW6vy+5xBmfedNNsHat+/ujISkFmDjRWXA/uB5pcM/7oEqS0nYtU1ixPY+ikjJVSkUkmk0DugM9OFKg7F7h1Q1ojrUDsfabcBoOa39Ka/mntUyylnMCx38ZQzNjKi3bSj1SUhrbVqxYwYoVK7wOI7oEE9J//tNZa/TKK2HGDOjb10lSa1oQ3+93Fr2PhqR07Fho2dLpws/Nhe3ba0xKbzijBzvzinhm/kaNKRWR6GVtHtZuxtpNwP3A/YH3wdcWrC2oTdO12jTdGOKMYawxvIKzTNQztWlHai+30E98nKFFStibckkUuOmmm7jpppu8DiN6VExIH30UHnoInnsONm+G3/0O5s93ZrW/807VbWza5CyjFA1JaUqKM7b0zTdhyRLnXHDmPVSalA7vlcXoftlM/3QdObkHnJOqlIpINLP2XqyN2PqGYSWlxjDUGJ4AdgLvAlcCaYQ55V/qzldQQkZaEsbof/pY9Oijj/Loo496HUZ0CE1Ib731yGft28MDDzhbfqanw8cfV91ONMy8r2jCBDh4EIJjh2uolALcOfY4ikvLeWneOueEklIRiXbGTMSYrzCmAGPKQl6l4TRVY5nNGLoDE4EJHJniXzETOgS8Fc5Dpe58BcVkpqtrL1YNDc7KbuoqJqR//OPRCWlFaWlOpfHbb6tuK9qS0pEjoWNH+Owzpyu/U6cjn1WRlPZo04yfntaNT1+fzS2gpFREopsxVwIvcGSyU51UWSk1hl8Yw3xgHc7+9r05etY9gSCyrWVCXQOR8OQGKqUSm5YtW8ayZWGtlNE4vf66k5BOnQq33Vb9tSecAN98c/Si9BWtX+9UU7OzIx9nbcTFOTs8gVMlrdirEUxKi47dd+SWs3vTKlAusAkaniMitWOMmWyM2WiMKTLGfGmMOaOaa0caYxYYY3KMMYeMMauMMVVUCY5yY+B4KHC0QE7g6/3A5nBirq77fjrOjk3BRLQEmAVcC4wKXmQtB8N5oESGr9BP62ZKSmPVlClTmDIlrJUyGqcvv3QStN/+tuZrBw1yJg1t21b55+vXQ48eRyd/XpsQ+Hu9Ytc9VFkpBWiZlsj4wc7OzV/urNVcARFp4owx44DHgYeBE4EFwPvGmC5V3HIQeAI4E+gPPAjcZ4yZXMOjTsBJREcfPmNtG+AenLzxwnDidjOm1OJMZGpnLT+0luc4kgWLR3IL/KqUxrBp06Yxbdo0r8Pw3vLl0K/fkfU7qzNokHP8pooVRqJlOaiKBg2CBx+EwGYJh4VsMxpqdM8MAJ5bsgN/aXl9RigijdNvgJnW2qestSuttTfjzAf6ZWUXW2u/tNa+aq39zlq70Vr7EvABUGV1NSA9cPyK4GL6xsQDfwba4CS6rrmd6PQzYJUx/M0YRgMazOih8nJLbqFfy0HFsMGDBzN48GCvw/Det9863fJuBK+rLCktL4cNG6IvKTXGWT3gxBOPPh8cK1pFUppQ5swN2HKghBf/G1bvl4g0ccaYJGAI8GHIRx8CrhbINsacGLj2sxouDW4jaoDAsiGcH3g+wDA3zwuqrjwxFfgJECz1tgVuCLwOVXVTrMnMzIy5rR7Lyi2/HlBKB/9m5s7d6XU4UgurVq0C4LjjjvM4Eu8k5OUxYudO1qelsdXlz+CwDh048NFHfH/66UedT9q7l+FFRawpK2NHjPw8n5mQwNa1a9lYSbxZX33FAODaExLYuXUFc+cqMRWRwxKMMUsrvJ9hrZ1R4X0WEA/sDrlvNxW72SthjNmGU+FMAO6z1j5ZQyw7gAycHHElcArwdoXPfTXcf5Qqk1JruRO40xjOxJl9/yOgZeDjNAJlWmPYBrxkLbeH8+Bo4fP5GDVqlNdhhGX93oNc++FnPD7+eEYN7uh1OFIL9957L0DM/UEUUYHvvecll9DT7c/gsGGkfvcdbUOvDyys3+f88+kTKz/PKSl0zc6ma2Xx7toFQLPuA3lk8UHeGjGYwZ1bNWx8IhKtSq21J9dT22cAzYBTgUeMMRuttS9Wc/3XwACciugLHFsZfT6ch9c4kMta5gHzjOEmnAGrE4GxHOnC7wDcBrGZlMai3OC+9xpTGrP+8pe/eB2C95Yvd44VF5WvyaBB8NZbUFDgzLQPirbloNxITq6y+z64o9PQPtnELTnIJ6v2KCkVEbf2AWVA6FIk2TgbHlXJWrsx8OVyY0w2zupL1SWlk4H/AQ5gbSHGtATGAaXAv4FHwgnc9eL51uK3ljes5VKgHc4yAAvCeZhEhrYYjX0DBgxgQOiM7KZm+XLIzHQWyHdr0CBnSajQLVrXr4f4eOhS1cTSKOQiKW3ZMp0Tu2Tw6ao9DRiYiMQya60f+BIYE/LRGMLL2+KA5Co/NSYZuBw4F2gRePhUrD0Ra4di7cNYWxbG82q3zai15FrL36xlBNALJ5OWBhJMSjOUlMasBQsWsGBBE/+bLjjJKZwlnKqagb9+PXTtGlt7xbtISklK4qy+bVi+PY89+ceuaSoiUoXHgKuNMdcZY/oZYx7H6dl+EsAY84Ix5oXgxcaYm40xPzTG9A68rgVuBV6q8gnWFgNPA8/iLP9UZ7VKSiuylg3W8kAkghF3fIWBSqm672PWnXfeyZ133ul1GN4pL3eqneF03YOTeDZvXnlSGktd91B9UloS+P2elMRZx7UFYO6avQ0UmIjEOmvta8AU4C5gGTACGGutDc6a7MKRiezgTIx6JHDtUpze8NuBmv5DtQFn5n1E1q6rc1IqDS+3wE9qYjypSfFehyK19Pe//52///3vXodRf+69F4YOrXr3pU2bnHGh4SalcXFHdnaqqLElpRUqpf3bt6BdixR14YtIWKy106213ay1ydbaIdbaeRU+G2WtHVXh/TRr7fHW2nRrbUtr7UmB+2tKNv+Mk5S62AGlZtrDLgb5Cko0njTG9e3b1+sQ6tcrr8CaNbBuHfTufezntZnkFDRoELz4olNtjYuD/fvB52ucSWliIsYYzjquDe9+sxN/aTlJCaoliEjUGI6zodIdGHMZ8A1HLxtqsfZat43pt1uMKSguZeH6fXTKSPU6FKmDzz77jM8+q2lN4hi1aZOTkAJ8GLp2c0AwKT3++PDbHzQIDhxwngOxOfMenKS0qIpxosHu+8AY2bP6tuVgcSlLN4W15J+ISH27CsgMfN0XuDJwLvi6OpzGlJTGmMc+WsOOvCL+57xGXmlr5MvVVH4AACAASURBVO655x7uuecer8OoHx995BybNz/ydahvv3X2qW/ePPz2Qyc7xXJSWl2lNDHx8CSw03tlkRQfx6er1YUvIlHHVPMKi7rvY8i32/bz3BcbmTCsC0O6ZtZ8g0StZ5991usQ6s+HH0KnTjB2rNONX1Jy7Kz45ctr13UPMGCAk6x98w1ceumRpLRHj7rF3dBSUiAnp/LPgklpQHpyAsN6ZPLJqj387oL+DRSgiEiNukeysSqT0sBOTq4FFtmXelJaVs7tbywnq1ky/3Ne092asrHoEWsJlFtlZTBnDlx2GZxzDsyYAYsWwYgRR64pKoK1a+GKK2r3jPR0Z5xqxUppdjY0a1b3+BtSTZXSpKPHjZ/Vty33/+d7tuQU0qV1WgMEKCJSgyOz+SOiukrpXAJbibpga2hL6ui5Lzbx/c58/jbhJFqmxtBajFKpOXPmADB6dLXbEMeepUudiUfnnAM/+IEzEemjj45OSleudJLX2lZKwenCXxrY+jkWZ95DzUtChSSlPzjOSUo/WbWbq0+PaHFCRKRujGkF9AaOnfBSYdZ/TWoaU1rdOIE6jRsQ97b6CnnsozWM7pfNeQPaeR2ORMCDDz7Igw8+6HUYkffhh07X+tlnQ0YGnHLKsZOdgpOcTjih9s8ZNAg2boT8/MaZlFZSKe2WlU6PrHQ+Wa31SkUkShiTiDHPAHuB/wKfhrw+Cae56qqbz4e8Pwdne9EvgG1AJ+B0nD1W3wvnoeKetZa73lpBnIH7Lz4eE87uNxK1Xnyxuq2EY9iHH8JJJ0FWlvN+zBh46CHIzXWSVHAmOSUnQ69etX9OMKFduhS2bWucSWklu1OddVxbXvzvZgr9paQlqXNKRDx3K3BNpBqrslJqLdcEX8AcnIR0nLWcaS0/sZYzgR8DWTiJqtSDd77ZwWdr9nLruX3p0ErLQDUWnTt3pnPnzl6HEVn5+bBwodN1H3TOOc56op9+euTc8uXQvz8k1CGpCs7Af/ttZ4H+xpaUVtJ9D04Xvr+0nAXrqpggJSLSsMbjDOFcFnhvgTeBImAdxxY4q+V2Sai7AsfZIedn4XTd3xbOQ8W9pz7fQL/2Lfjpad28DkUiaPbs2cyeHfrjFOM+/dQZK1oxKR02zFn2qWIXfl1m3gd17gytWsGbbzrvG1tSWkn3PcDQbpmkJ8XziZaGEpHoEPzle2TmqrVXAD/CmZn/bjiNuU1KuwWOk0PO3xg4dg3noeLenvxiTujYkvg4dds3JlOnTmXq1KlehxFZH37ozIw/7bQj5xIT4ayzjiSlOTmwc2fdk1JjnGrptm3O+1hOSivbirWKpDQpIY4RvbP4dNUeysrdzkMVEak3wXFGm4EyAIxJxelhjwfuC6cxt0lpYHsW/mAMu41hmTHs5v+zd99hUpXXA8e/BxZmYXdpuwgKIprYG7YQjUY0FtRoVJLgz1iwxChRo0Zj70YxlmDDrqhowBqDUcCGSSwoICp2BRt9cWm7LNvO74/3jjs7TLmzO/XO+TzPPDNz25ydWXYO521wLa5U+1n8U9cnIqNFZL6I1IvILBHZK8nxXUXkKu+cdSLyjYicGXXMCBH5yNv/kYgckUpM+UhVWVHXSK8yG20fNBMnTmTixIm5DiO9XnwRhg1zyVakAw5wg5K+/DI9g5zCwk345eXQt2/Hr5dt4fcpvHpTpDh9SgGO2GkAi1bW8/Ts7zIYnDHG+FLj3XcDwkvOXQr82Xuc0uABv0npxUALrqm+CtjeuxdcUnqR3xcUkZHALbiEdifgDeAFERmU4LSJwHDgFNwyVr8B3o+45u7AJOBRYIh3/4SIDPUbVz6qa2imobmF3t1tnfug6d+/P/37B2gmhfnz3dyjkU33YeFt06a5QU7Q8UoptCalP/rRDysfFZRwUhqrCT9On1KAA7ftz44b9+KmaZ9R39icwQCNMSaped79AGA2Li88H7galx/OT+VivpJSVZ7DJYUzvBcJJ6NvAQeopjT6/hxgvKreq6ofq+oZwCLgtFgHi8gBwC+Ag1X1RVX9SlVnqOr0iMPOAl5V1b961/wrbp7Vs1KIK+/U1DUA0Lu7VUqDZvLkyUyenFJXm/wWXk40VlL64x/D4MEuKf3gA6ishHQk5JFJaSEKJ6X19evvi9N8DyAiXHTQVixeVc8Dr6f0994YY9LtRVxr+VbAjbQWMMOVgqtSuZjfSimqvKzK7kAPYGOghyp7qPqfg0pEugK7AFETFzIN2CPOaYcD7wDniMh3IvK5iNwqIpHLt+we45pTE1yzIKyoc816vaxSGjg33XQTN910U67DSJ9p09zgoy23XH+fiJsa6pVX4N13XZU0HZXNbbd1S3VuvXXHr5ULiSqlCZrvAYZuVsl+W/fjzle/ZPmaOIOljDEm01QvR3VrVJ9F9WVgb1xr+E3Az1GdlMrlfCelACKUANsBO6hSl8q5nipcx9clUduX4KacimUzYE9gR2AEcDquajs+4pj+qVxTRE4RkZkiMrOpqSmV+LMqXCntU2ZJadA8+eSTPPnkk7kOIz2amuDll12VNF6yecABbsqoWbPS058UXEL61ltwXoFO/lFa6u7jJaVxKqVhFxy0JbUNTdz2yhcZCM4YY9rlbVTPRvU8VFOeLtR3UirCb4AFwJt4Q/xFeFmEeSLEaLNLm064rgJHe832U3GJ6QgR6deeC6rqPaq6q6ruWtKRuRIzrMarlFrzffBUVVVRFZ5gvtBFLi0aT3jJUUhPf9KwHXeEnj3Td71samef0rAfb1DByN0GMeGtr/mqujYDARpjjA8i2yDyJCIrgXpEViDyBCLbpHopX0mpCHsB/6B1cFO4HPJv3HRRv4595nqqcVMGRCeT/YDFcc5ZBCxQ1ZUR2z727sODoxaneM2CsMKrlFrzffA8/fTTPB2eY7NQqMKcOfDvf7e93Xtv69Ki8fTpA7vu6h6nMyktZMma75MkpQBn77c5XTp34oZpn6Y5OGOM8UHkJ7jxRkcAFbj8sAdwJDADkd1SuZzfMuGFuAT2E1xn1rB/4zq27h7rpGiq2iAis4D9gScidu0PPBXntNeB34hIuaqu8bZt4d1/7d2/6V3jhqhrvuEnrnxVU+v1Ke1mldKgufXWWwE48sgjcxyJD199BY8+ChMmwCefxD5mjz3cAKZEfvlLN9Bp223THmJB6kCf0rANepTy+59vxq0vf87Je9aw06DeaQ7SGGMSuhko8x43AsuBStz8pWXe/oTTfkbym5T+FNeEfijwecT2yKkA/LoZeERE3sYlnKcCGwF3AYjIwwCqepx3/GO4Oa8eFJErgF64TrRPqmp4WZNbgP+IyAXAP3EZ+z64vqgFq6augYrSEko6p9T11xSAZ599NtchxKcK8+bB1KkwcSL8979u+157wdlnw5Ah6/cd9bOO/V/+Akcf7eYVNR1uvg875eeb8diMr7li8kccMzTRzHrZNahPd4ZuluQ/KsaYQrcLLj+8DbgQ1bXe5PnX47pa7pLKxfwmpeEs+Juo7b28e9+LsqvqJBGpxC1duiEwFzfdU7jqOSjq+DUish/uB34HN1HrP4ELIo55Q0SOAq7BTT/wJTBSVWf4jSsf1dQ12BylAdUz3/pBVle7wUovveRuX33ltm+1Ffz1ry6ZHDy4Y68RChXu9E2ZkIbme4DyUAnnD9+Kvzz1Pud9uyKNAXZMl87CJ1cfZKvRGRNsNbjukpeiuhbAS0wvwSWly1O5mN+kdAFuKdHoZvpzvfuUlhZR1XHAuDj7hsXY9ikkHkylqk8CARnO7NTUNdogp4CaNMnNkjFy5MjMvUhLi1vSc0CShozFi2GbbaCmxg0a2mcfOPdc2G8/2GKLwpyYvhCkKSkF+M2uG7P3ln1Z19iSpuA65omZ33LrK1+wZl0TPa37kTFB9jBwHq5r5zsR28PzAz6QysX8JqVTgT/gKpQAiPAJsDmubDs1lRc1/qywSmlg3XnnnUAGk9KFC+G44+DVV2H27NaJ5mN56imXkD73HBx4IOTxjBSBkoY+pZE2qChNQ1DpMaC3azyzpNSYwPsSVw2djMi9uBb1QcDJuILm14gc98PRqg8nupjfb59rcCPsK3FJKLiEVLxgrvMfv/Grpq6BzarKkh9oCs7zzz+fuYtPngwnnABr17ppmCZMSJyUPv64q5QeckjmYjLri5eUqqbUpzQflYdcIrqmPn/ngTbGpMXdtOaFsZacvzfiseIqq3H5XWZ0AfAz3KpJ4SWkWrzne3n7TZqtqG206aACqnv37nTv3j29F62vhzPOgMMOc6srzZ4NBx8M//gHNMdZI33RIjeQ6Te/SW8sJrl4SWlzs0tMCzkpLXX1jjXrGnMciTEmCySFW0K+2+lU+QwYLkIp0Af4XpUYizabdGhsbmH1uiZrvg+oCRMmAHDMMcek54KffuoSyw8+cCPkr7vOJT1HHw3/+pdLPIcNW/+8p592CZAlpdkXLyltcPMTp9p8n0/KQ+6rZbVVSo0JuhPSeTFfSakIPYGeQJ0q1cBCb3sV0B1YqcrKBJcwKQqve9+7rHC/mEx89913H5CmpPSVV2DECJfEvPACDB/euu/QQ90UTI8+GjspDTfd29yh2RdeZrQ+6v/24aS0gCulFT9USi0pNSbQVB9K5+X8ToD5ADAfODpq+1He9vvTGZSx1ZyC7sUXX+TFF1/s+IUeeMANThowAN5+u21CCtC9OxxxBDz55PoVOWu6z614ldJGr8m7gJPScKXU+pQaU2REOiEyEpHzEBmS6ul+k9Kh3n30qktP4/oIDMWkla17H2xdunShS0eaZ1ta4IIL4KST3Lryr78efx7Ro492a9O/8ELb7dZ0n1vJmu8LOCktC1ml1JiiIDIGkaWIXO5teQK36NEY4B1EEqw/vT6/SWlf7z56ZuaVUftNmtR4lVLrUxpM46+/nvF33dW+k+vq4Le/heuvh1NPdevPJ5qMf7/9oG9feOyxttufeMKa7nOpUyc3/VaA+5RaUmpM4O2Nm5npP4gMwK2oGR7U1JmIhY788JuUrvbuoyewDz9fg0mrcPN97zJLSgOnro7xF13E+DFj2nf+ySe7KufNN8O4ccnnFS0pgZEj3VRRq1a5bYsWwX/+Y1XSXAuFAtl837mT0L1rZ2u+Nyb4wsv0fQjs5j2eQOsAqJ1SuZjfpHQ2Lut9QIRLRBghwiW4vqQKzErlRU1y1nwfYK+9xvSWFqbvu2/q506f7qZ4uuwyN8re72pLv/udG1DzzDPuuTXd54dYSWkAmu/BVUutUmpM4IWb6b7HreqkwGRcEz5Aj1Qu5jcpDbcz9gCuBB737ntF7TdpUlPbQNeSTnTr0jnXoZh0mzLF3S9PaUlgV0E74wzXd/T881M7d+hQ2HRTNwofrOk+XwQ5KS0tYbUlpcYE3ffe/RG0tp5/BlR4j1elcjG/k+c/DdxM7ElQb1LlmVRe1CRXU9dA7+5dEFt3PHimTOFe4N4PP0ztvHHjYO5cGDsWunVL7VwRN+Dp5Zdhzhxrus8XiZLSAu5TClARKrHme2OC7z3vfiKuf+lqYC6wmbf9m1Qu5rdSiirn4kbZ/xW4z7sfqspfUnlB409NXaMNcgqi+fPhs8+YBExatMj/eUuWuCb74cPdik3t8bvfuVH7xx5rTff5IqB9SsFVSq353pjAGwPU01qs/BuqzcAvvf1vpHIx3ys6AajyDvBOKueY9llR10Av608aPFOnAvDSvvvC++/7P+/8812f0Ftv9d+PNNrWW8OQIa5Sak33+SHIzfehEqpX1+U6DGNMJqlOR2Qr3CCn+ai+6+2ZBLwIzEvlcr6TUhEqgIOBTYDS9ePiqlRe2CRWU9fI5huU5zoMk25Tprg+obvv7gYttbS4qYESeeMNeOghuPBC2Hzzjr3+737nklKrkuaHADffl4e6WKXUmGKg+i3wbdS2j9tzKb/LjO4GPI9b8z4eS0rTyFVKC7tSYqI0NLglQY8+mnGffAItLYxeuRJ6945/TnMznH46DBwIF1/c8RiOPx7efNNNum9yL8DN9xWlJayub8x1GMaYdBM5DgDVh394nIjqw34v7bdSOhY3OWrcl/T7giY5VWVFXaNNBxU0b74Jq1fD8OFMvtwtfjG6ujpxUnrfffDuuzBpEpSVdTyGvn3hqeiF2UzOlJZCbW3bbQFqvl+zrglVtQGbxgTLeKAFeNh7nCgHVO84X/wmpTt4F34Nt9RobZIgTAesXtdEU4vaQKegmTLFTWS/77680LUrHHKImxYqUZP844/DDjtYc3tQhULw/fdttwUlKS0toUVhbWMz3bumNHzBGJP/JM7jDvH7l2IF0B04UnW9pUZNmq2odU1eNtApYKZOhT32gB49oNJreEg2V+mSJbDllu0f3GTyW6D7lHpLjdY3WVJqTLCcEOdxh/n9S/Ewbv3S7YD/pTMAsz5b9z6AFi92zfDXXgvALc89B8CfkiWlS5fCXntlOjqTKwHvUwqu5WeDHMdijEkj1YdiPk4Dv0npV8BK4FkR7gc+Bdr0YFf132fAJFZj694Hz7Rp7v7AAwF4eeZMAP5UXR3/nOZmqK6GDewrPbACPiUUYBPoG2N885uU3k1rH9I/x9ifUkdWk9gKW/c+eKZOdcnlkCEA/Ov5513zbKJK6fLlbpL7fv2yFKTJumJovrdpoYwJFpFU5h5VVH/k9+BUOvpYp7Ysseb7gGlpcZXSgw5qnZNUBPr0SZyULl3q7q1SGlxBrpSGm++tUmpM0Aym7WD3cH4YPQBeYmxLyG9SmtaOrCaxmrpGRKBHt8KulBjP7NmuGd5ruge48cYbQYRzLSktbkHuUxpyf7+sUmpMIMUqVHa4eOkrKVUlrR1ZTWI1tQ307NaFzp2sOB0IU6a4yugBB/yw6c0333R9RhMlpUuWuHtLSoMrnJSqts6w0NDgHnfunNvYOihcKV1jE+gbEyyqrcsQivwI+I93uxj4DhgIXAvsA+ydyqWTrG9ocqGmrsGa7oNkyhTYZRc3cb3nqaee4qk993QV1HisUhp8oZBLSJsiqokNDa4/aYFPA1YWckm1VUqNCbRbgf7AaajOQ7UB1XnAqUBf3OJLvvlOSkU4RoTZItSK0Bx1s786abSirtHmKA2KNWvgrbfaVEl/UFmZvE9pSQn06pW5+ExuhULuPrIJv7Gx4JvuAUIlnela0onVlpQaE2ThSuhmUdt/7N3vmcrFfCWlIvwWN7p+R6Abrt9A9M2kiVVKA2T5ctdM/6O2gw/HjBnDmC++aB1hH8vSpa662skaNAIrVlLa0BCIpBSgIlRiU0IZkyMiMlpE5otIvYjMEpG4k16LyJEiMk1ElonIahGZISKH+XiZNd79C4jciMhZiNwI/Dtqvy9+v+3+6N2v9e4VCJd4VgBfp/KiJjGrlAZIeF3z7t3bbJ4zZw5zVqxwyUhdXexzly61pvugKy119/X1rdsClJSWl5ZQa5VSY7JOREYCt+D6du4EvAG8ICKD4pyyN/AKcIh3/PPAM4kSWc8juMJkFXA2cJN335d2TBfqNyndwbv4fuENqvQFLsdNon9oKi9qErNKaYCEE86ysjabJ06cyMQzz3RP4vUrtaQ0+OJVSgt8jtKw8lCJ9Sk1JjfOAcar6r2q+rGqngEsAk6LdbCq/klVx6jq26r6hapeCcwCDk/yOhfhEs9YrecPe/t985uUhr9RZ+PNOSVCZ1xG3BfX0dWkwbqmZuoamm3i/KAIV0qjklLA9SmF+P1KLSkNvgD3KQWXlNo8pcZkl4h0BXYBpkXtmgbskcKlKoCahEeoNqI6CtgaGA1cikt8t0b1BFRT+gPgd57SVUBvXPa72gv0INzSowBDU3nRfNKnTx+mT5+e6zB+0NSi/Hn7JjZaO5/p0xfkOhzTQX1mzGAHYPYnn7Aqom/oww8/TOmSJYwD3nvlFWpWrVrv3D0XLWLRunV8mUe/nya9qj7/nO2Ama+/zhpvCrBtvvuOsqYm3gnA535wVR2NzS159TfWmAAoEZGZEc/vUdV7Ip5XAZ2BJVHnLSGixTsREfkjbmqnR3xFpPopbgn6DvGblC7EJaUbAB8DPwGejdj/fUcDyZXvv/+eYcOG5TqMH3yyeBUnT/svdxy9PcN22DDX4Zh4vvwS/vY3uPXW1mpXLF7T/M577QXbb//D5vvuu48GL0ndceBAiP4drKuDtWvZeJdd2DiPfj9NmnndO3bdfnsY6v3fvlcv6Nkzr/4utddZE9/l3W9X8Npvh+U6FGOCpElVd83UxUVkBHADMFJVUxsz5JYgTWlp0Uh+m+/fxVVJh7J+3wHAJtdPl5paW/c+76nCKafAPffAxx8nPjbOQKcJEyYw4f773ZNYzffLlrl7a74PtqA335fa6HtjcqAaaAb6RW3vByxOdKKI/BpXHT1OVSe347UHe7d28VspHQ38BVitSp0IPYGRQBPwDHB9ewMwba0Ir3tfFowvpUB6/HF45RX3OJx0xhNnoBMAffq4+1gDnWzi/OIQ8CmhykNdbJ5SY7JMVRtEZBawP/BExK79gafinSciv8UVGY9X1SczG2VsfpcZrQVqI56PAcZkKqhiVlMXrpQG40spcFavhnPOgR49YNWq5ElpnIFOl112GQBX9ewZu1JqSWlxCHxS2pmGphbWNTUTKinsZVONKTA3A4+IyNvA67gVljYC7gIQkYcBVPU47/lRuArpucB/RKS/d50GVc1aF824SakI8eayikmVbzoejqnxKqU2T2meuvpqWLgQ7rwTTjvNf6U0qvn+22+/dQ+qqhInpf2iW19MoMRLSisqchNPmpWH3FdM7TpLSo3JJlWdJCKVwCXAhsBc4OCIPqLROd6puJxwLG2XBn0NGJbZaFslqpR+hTf9kw+a5FrGp5raBrp16UxpF/sDnnc++gj+/nc46ST4xS/cNj+V0lAIOrf9PB988EH3YOjQ2EmpNxKbvn07GLTJa4HvU+r+c72mvok+1iXJmKxS1XHAuDj7hiV63oEX7dAShMkSSVs+NMtq6hptkFM+UoXTT3cVrOuuc4kD+EtKo6qkbVRWtg5qirR0KZSXJz7XFL7AN9+7r5jV6xpzHIkxphAkSkptRH0OrKhroJf1J82+xkaYORNeew1WrnSV0D33bF0G8vHH4dVXYdw4V71c6U3R66f5PsYgpwsvvBCA6yorY4/gt4nzi0P49yugSWlFqfuKsRH4xhQZ13VgGdCCqu+W9LgHqnJCOuIyqampa6B3mVVKs+LLL2HiRJeIvv56a//PkhIYMwa6dYN99oEDD4Trr4edd3ZTQUFrorlmTeLXqK2NmZQuDzfZV1bG71NqSWnwFcEyo4AtNWpM8Uqpxd36geaZFXWNbNirW67DCL5p0+DXv3aj6bffHk48EfbeG37+c5dETp8OU6fClCnw/PPunKeeau0bWlLiqll+KqUxmuDvucdbfOOaa1wM0dWxpUthk006/nOa/BZOSuvrW7cFqk+pJaXGBI7IaB9HxZgHMTnfSakIWwJ/ALYEorMmVeUX7QnAtFVT12B9SjPt3nvdyPltt4Vnn4XBg9c/5pBD3A1g3jxXzdxtt7bHlJf761Maa47SsMpKd//999C/f+v2pUvXfz0TPAHvU1oR7lNqzffGBMnt+B8InxJfSakIuwDTgVijLoQMBVdsWlqUlWsbbY7STGlpgYsuck3xw4e7fqJ+pt7ZbDN3i1ZW5i8p7dVrvc3nnnsuADf+5CduQ3V1a1La0uIGP1nzffB17uxuQW2+t0qpMUGW9sHwfiulF9HOUqzxb1V9Iy2KDXTKhLVr4fjj4Ykn4NRT4bbbXBN8R/hJSuvqYMCAGOGsdQ/CldLIfqUrVkBTkyWlxSIUCuyUUN26dKaT2EAnYwKmAeiCm4h/SZxjugPnpXphv9/Ke+CqoaOBO73HOwLXAFvhlhw1HdS6mlMwqiR5QxWOOML1I73xRrcik6ThP3h+K6Uxmu/vuOMO9+C999x9ZFJqqzkVl+ikNEDN9yJCeajEKqXGBMscYDfgVVSfiHmEG32fclLqd5JTr5zDo+ENqswFTgG2AM5O9YXN+sKrOVnzfZpNnuwGLf397/DnP6cnIQWXbCYbfR9noNMPYlVKLSktLpFJqWqgKqUAFaVdrE+pMcEyA9d0PzTdF/ZbKV0LlAP13uNSb+BT+Bv5sHQHVoxW2BKj6dfUBBdeCFtsAaP9DBhMQVlZawIZT5xK6VlnnQXA2OuucxssKS1ekUlpeFGGgPQpBbxKqU2eb0yAXA08AKxIcMz3wKapXthvUroUl5T2wS0/uhXwKhD+729Lqi9s1ldT6/5w23J8afTQQ2550KeeSv8XfXk5zJ8ff79q8hWdunVzt+rq1m2WlBaXWElpgCql5aXWfG9MoKhWA9VJjlHg61Qv7Tcp/QDYDNgBeA7YGugXfmlgWqovbNZX80OlNDhfSDlVVweXXw4//anrU5puyfqUrlvnEtMYldKxY8e2PqmqWr9SKtLatG+CLTIpbXB/AwKVlIZKfmgFMsaYRPz2Kb0SOBpXJb0Gl4SGO+a9DPwp7ZEVoZq6Bjp3EnqU2poGaXHrrbBgAfztb+nrRxopWVIa3pdonlJYf1WnJUvcto7ODmAKQ9CT0tISVlul1JjgELkckfXnOox/fC9ELvdzqK9vPVXeA96L2DRchF5AkypJRnoYv2rqGunVrQuSiQSq2Cxf7pYKPfRQ2GuvzLxGsqQ0vGxpjOb7P/7xj4A3Cj86KV26FPr1W+8cE1ClpYHuU1oRKqHWklJjguRy4BxEngQmAa+j2vbLUKQM+BlwFDAC1wX0ymQX7kgppiuQZD4ck4oVdQ02yCldrr3WLd8ZHkiUCWVlLploaopd1UxQKe3WLWJRtMpKmDOn9bmte19cQqHWZUaDWCkNldg8pcYEy0fANsAo79aCyFe09jOtAgbT2hovwId+LpwwKRVhZ1yWYOFCFQAAIABJREFUWwr8U5VXRDgZuA436GmdCONUOdf/z2Liqam11ZzS4quv4PbbYdQot5RopoSTzdpa6Nlz/f3hpDRGpfTGG29sfRKrUrrTTmkM1OS1UMgtmADBTEpLS6htaKa5RencyVqBjAmAHYCTgHOBzYHOwI9wY4+g7UpPXwI3APf5uXDcpFSEPXH9RcPH/FGEG4C/4AY3CS5ZPVuEL1S5y+9PY2KrqWtgYO8EI7WNP5ddBp06wZVJWwo6przc3cdLSsPN98n6lFZVQU2NW160UyerlBabWH1KA9R8Xx5yXyG1DU30KA3Oz2VM0VJtAe4F7kVkGHAgbjJ9b61slgDvAFNRfTWVSyeqlJ6HW0Yqehu4hLQaV6IV4FiwpLSjVtQ1sv0A+6PdIS++CI884uYmHTgws68VWSmNJUHz/SmnnALAPffc4yqlLS2uWlZe7u4tKS0eAZ8SqsIbuLmm3pJSYwJHdTowPV2XSzT6fldcRXQqbnnRF3AJqAL/p8oGwO+8Y7dJV0DFaunqepaurrdKaUfU1MAJJ8DWW8Oll2b+9ZIlpQkGOlVWVlIZnvIpclWnZcvcY0tKi0fQR9+HXCJqc5UaY5JJVCmt8u5HqrJKhH8ANd62p737p3BLj1ZkKL6i8cIHi2lROHj7/skPNrH98Y9uOqV//ctNSJ9pHaiUXhc5ACuclFZXu5HYYElpMQl6UupVSm2pUWMCSOQnuCLmx6i+isj+wK3AIGAKcNx6I/MTSFQp7QKgyirvfmV4hyqN3n14RmTrvd5B/3pvIVv1r2Dzfpbft8vEifCPf7jJ8nfeOTuvGU4218SZFS3BQKc2qrz//y1fbqs5FaMi6VNqlVJjAukvwG3AFoh0wRUqtwC6AYfjpo/yLemUUCJc5mebab/vauqY9XUN5x24Za5DKUwLFsBpp7mVmy64IHuv67f5Pkal9IQTTgDgwQcfbNt838n7f6IlpcWjiPqUGmMCJzxVzCvALrhW9kXAQu/5r3CJqy9+5imNzHI1xjbTQc+9vwiAQ3fYKMeRFCBVOPFEV2F6+OHsroIUOfo+lgSV0o033rj1SWRSqt4/MUtKi0fQm+9/qJQ25jgSY0wGhFd6+RbY23s8Bngcl5xuksrFkn2DW7N8Fkx+byE7btyLQZU2yCll48bBtGnufvPNs/vafiqlXbrEbIq96qqrWp/06OGS6epqaG52SUqFdeMoGqGQm32hqSmYSan1KTUmyMLFyu7Adt7zD2kdg9ScysUSJaUZnuTRAHy5bA0fLlzFpb+0CQxS9s47cN55cOCBcOqp2X99PwOdks1RCiACffq4Sum6da5KakvNFo/w4LZ16wK5zGhZV+tTakyALcBNoD+Z1pmYPgTCTb/VsU6KJ25SqmpJaTZMfm8hInDI9hvmOpTC8uWXcMghbo34hx7KTRLnJymNM8jpmGOOAWDChAluQ1WVS0rr6qzpvtiEQu5+3bpAVko7dxLKuna2PqXGBNMzwPnAT3Gt62+jugSR33r730/lYlnsgGeiqSqT31vITwb3oX/P0lyHUziWLYPhw12T55QpLjHNhc6dXUIRb/R9XV3cSumWW0YNagsvNbp6tSWlxSbgSSm4JnyrlBoTSFcCPYC9gPnAOd72QbhVQSemcjFLSnPoo0Wr+HJZLSfuuWmuQykctbXwy1/Cd9/BK69AdHKXbWVl7Wq+vzR6cv/KSlf9XbECttsuzUGavBZOSuvrAzklFEBZqITVlpQaEzyq9cAfY2y/Ebgx1ctZUppDk99bREkn4aDtrOnel6YmOOoomDkTnnoKdt891xG5EfiJBjolm6M0rLISZsyA77/PXeXX5EZkpTSAU0IBVIRKrPnemCAT2R7YDzclVDXwIqpzU72MJaU5Em6633PzKvqUBesLKCNU3YpNzz0Hd9wBhx+e64icZJXSOKPojzrqKAAmTvRaNqqqYPFi93Na831xseZ7Y0yhEikB7gOOjbHvYeBkVH2PwE+0olPGiMhoEZkvIvUiMktE9kpw7DAR0Ri3rSKOGRXnmLztqDn7mxUsWLHW5ib1a9o0uOceOP98GD0619G0SpaUxqmUDhkyhCFDhrRuqKy0OUqLVaykNGDN9+VWKTUmqK4BjsMNcoq+Heft9y3rlVIRGQncAowG/ufdvyAi26jqNwlO3Rb4PuL5sqj9dcCPIjeo6+uQlya/t5CuJZ04YFtrqvXl9ttds3bk/J75IFFSmmCg0wXRK0+FJ9AHS0qLTXTzfadObhBdgJSHulil1JhgOg43N+kyXMX0G9wgp5OBDYBRwIV+L5aL5vtzgPGqeq/3/AwRGQ6cRuLAl6pqovmuVFUXpyvITGpuUf79wSL23XIDKkqDVRHJiPnz4d//hosvzr9mzbIy1+wei995SsGS0mIWXSnNt9/xNKgoLWF1va3oZEwA9fTuD0F11g9bRZ4FZuBG5vuW1eZ7EemKWwt1WtSuacAeSU6fKSKLRORlEdknxv5uIvK1iHwnIs+JyE4xjskLr39RzbLV6zhsiDXd+3LXXa569Ic/5DqS9SWrlMZpvh8xYgQjRoxo3VBV1frYktLiUgRJaXnI9SnVcBcVY0xQzPTuP4/a/ql3/3YqF8t2n9IqoDOwJGr7EqB/nHMW4aqoI4AjcT/oy1H9UD8FTgR+BfwfUA+8LiIx150UkVNEZKaIzGxqyn6T0jPvLqCitIR9t7LkI6m1a+G+++BXv4KBA3Mdzfrijb5XTVgp3X333dk9cvaAyEpp375pDtLkteikNGD9ScENdGpRWNuY0oqDxpj8dzawBriG8Dged381sIrWeUt9yfvR96r6Ka0ZN8CbIjIYOA/4r3fMm8Cb4QNE5A1gDnAGcGaMa94D3ANQVlaW1f+6165rYsrcxRy+0wBKuwSr31hGTJrkpkk6/fRcRxJbvEppQ4Nbxz5OpfTcc89tuyGclPbqFchKmUkguk9pAD//8lDrUqPdu+b9144xJhGRedFbcHOVnoLIcqAS6ALUAk8SNd4nkWz/dagGmoHo0T39gFT6g84Ajoq3U1WbRWQmbj3WvDJl7mLWNjZz5M4Dch1KZq1e7RKzyApge9xxB2yzDQwblpaw0i5eUlpX17rfj9693b013RefUm+SkAA331eUeklpfRMbxJ4lzRhTOAbjBjeF1/cOP+4KbBixrRzw+SXoZLX5XlUbgFnA/lG79gfeSOFSQ3DN+jGJiAA7JDomV555dwEb9+nGrpv0znUombNsGey6KwwaBGPGuC/b9nj7bTdR/ujRuVnb3o+yMpdINEYN4ggnqnGS0sMOO4zDDjusdUNJiauSWlJafIqkTylgI/CNCYZvvNvX3i3ycfS2RLMqrScX7Sg3A4+IyNvA68CpwEbAXQDiJltFVY/znp8FfAV8iMvCjwEOx/UxxTvmcuAtXEfbHrgm+x1wfVHzxqKVa3n9y2rO2HdzJF+TrI5aswYOOQS++cZVNy+8EB58EG69FQ48MLVr3XGH67N57Ppz8uaNcNJZW+uSyrBwpTRO8/0vfvGL9TduuCFsZIPfik4x9CkNtVZKjTEFTnVwpi6d9aRUVSeJSCVwCa7MOxc4WFW/9g4ZFHVKV+AGYCCwFpecHqKqz0cc0wvXR7Q/sBJ4F/i5qqY06ivTnp2zEFU4YqeANt03NMCIETB7NjzzDBx6KEyZAmeeCcOHu1WYLr7YJZqRevZ0CVmkZctcf9KTT4YeKc0okV3xktIkldI//elP62987DH3XpjiUgx9Sr3m+9VWKTXGJJCTHueqOg4YF2ffsKjnfwP+luR6Z+NGgOUtVeXp2d+x86BebFqVUheLwtDSAqNGuZWX7r/fJaTgktEPPoC//x2uvhr++c/Y5x9wgGumP+QQ15R9//3uSzqfVm+KJZxgR/crDT+PUymNKXKFJ1M8wklpfX1gm+8rQq76a5VSYwLILTV6MLAl0G29/aq+V72xYZBZ8uHCVXy2ZA1XH75drkNJP1U45xz4xz/guuvgxBPb7g+F4IILXDP8//7Xupxm2Kefwr33ukrqxhvDKae4aaD22ccNcspnkZXSSEkGOh100EEAvPDCC5mKzBSKkhI3D2+Q+5SWWp9SYwJJZANgOi4hjceS0nzzzLsL6NJZOHSHDZMfXGhuugluuQXOOsutTR/PgAEwcmTsfRdfDJMnw513wqWXtl4338VLSpM03x8ariQbA+4/buHm+wD2KS0LuenvLCk1JnCuBLZKsD+laTctKc2CpuYWnp2zkH232oBe3QNWBVm1Ci6/HA47zCWR7R3AVVICRxzhbp99BjNmuMf5LlmlNE7z/eh875ZgsiuclDY0pNblo0CESjrTtaQTq6353pigOQCXeI4HTvAe/wk3T7wCY1K5mCWlWfDfL6qpXrOOI3bKwxWJOmrSJJeAXXSRa4JMhy22cLdCEE5K16xpuz1JpdSYNiKT0gA23wNUhEpYs64x+YHGmEISHrl9AS4pBdXbEXkV+AA3SN23bC8zWpSenr2AXt27sM9WAVw+8r77YLvt4Cc/yXUkuZGs+T5O1Wu//fZjv/32y2BgpqBEJqUBbL4H16/UBjoZEzjhtYOXA+5/nSJ9cXOUApySysWsUpphq+sbmfbhYn6z60BCJQFbVvT9990E92PH5u/k9pkWb/R9koFOI+P1rTXFKbJPaUArpeWhEutTakzwLMdVS3viVuYcCDwK1Hv7U1opyJLSDPt6eR2VZV05cucANt3ff7/7Aj3mmFxHkjuJKqUlJXETjN///vcZDswUlCJovi8PlVifUmOC51NcUvoj4D/A74Dw6jAKzE7lYpaUZth2A3ryv/P3DV4hsb4eHnkEjjyy4+vbF7Jw83ysSmkAB6yYDCktDXxSWlFawqKV9ckPNMYUknuBL4BS3Ej8A4BwX8VlwFmpXMz6lGZBp04SvGVFn34aamqg2Ct+nTu7hCJWpTTBIKdhw4YxbNiwzMZmCkfAp4QCa743JttEZLSIzBeRehGZJSJ7JTh2QxF5TEQ+EZFmERnv60VUH0f1NFT/h+oXwObAEcChwJaovptKzFYpNbBkCVx1lZu0fscd/Z1z332w2WZufftiV1YWe/R9gqR01KhRmY3JFJZiaL63gU7GZI2IjARuAUYD//PuXxCRbVT1mxinhIBq3BROKQ1OakN1FfBse0+3SmmxW7ECDjwQxo2D3XeHCROSn/PFF/Dqq3DSSembBqqQlZWl3Hw/atQoS0xNq2JISkNdWG2VUmOy5RxgvKreq6ofq+oZwCLgtFgHq+pXqnqmqo4Hvs9inG1YRlHM6urgl7+Ejz5yyehPfuKWAj3jDPflGM8DD7hk1JIqp7w85eb7xsZGGhttzkbjCYVcP+0AJ6UVpSU0NLWwrqk5+cHGmHYTka7ALsC0qF3TgD2yH5F/Rd9836dPH6ZPn57rMLJOmprY7pJL6PP223x06aUsGzAAufRSNttgAza+/XZWvvoqH15xBQ1VVeud99O772b1T3/K3M8+c6svFbmdW1po/PZbPoj4Pdpp0SJaQiHei/O7ddZZru/32LFjsxChyXfbrFxJeU0N3Zubmb9gAV8H8G9S/7oG/rx9E/964aV29bEXgZ7dulDSKWD9841JXYmIzIx4fo+q3hPxvAroDCyJOm8JkNcTZBd9Uvr9998X34CTlhZXEZ0xA+6+m21Pieg+8otfwKRJ9DzpJPY4/XQYPRr23BOGDnWVv2efhe+/J3T++cX3vsXTvz80N7d9P0pKYODAuO/RueeeC2DvoXEefBA+/hiATbfYgk0D+Hvx38+Xce4Db6Pa/ib8M38xmHP2LZDV3ozJnCZV3TXXQWRC0SelRUcVzjwTHnsMrrvODW6KNnKkW6XppJPgiivcOZ07w047ubXuN9wQDj4466HnrbIyWLSo7bYkzffHFPPcrmZ9oRCsXu0eB7T5fq/N+/LhlQfS1KLtOn/fG19j2WqbUsoYH6pxKy31i9reDzfBfd6ypLSYNDfDn/4Ed9wB550H558f/9htt4W33nIDod58E15/Hf73P/jwQ7jsMlcJNE6s0fdJBjrVeSs+dbe5TA24pDTcLzmgU0IBdO/a/r8bVeVdWbY6QV93YwwAqtogIrOA/YEnInbtDzyVm6j8scyiWKxb51ZeevJJOPdcuP56f0uD9uoFBx3kbuCqpkGbc7WjYo2+T1IpPdirNBdjf2YTQyjU+jigldKOqioPsbx2Xa7DMKZQ3Aw8IiJvA68DpwIbAXcBiMjDAKp6XPgEERniPewBtHjPG1T1o2wFbUlpMVi5Eo44wk3jdNNNcM457b+WJaTrizX6Pkml9LTTYs7KYYqVJaVJVZV35etvapMfaIxBVSeJSCVwCbAhMBc4WFW/9g4ZFOO06InuDwW+BgZnKs5olpQG3eLFrso5d66b9ul3v8t1RMETXSltbHS3BJXSkSNHZiEwUzBKS1sfW1IaU2V5iOVrrPneGL9UdRwwLs6+YTG25bzqZElpkKi6PqBff916GzsWli6F555zk+Sb9Csra01Eu3RpTVATJKUrV64EoGfPntmI0OS7yEppgPuUdkRVeYi6hmbqGpo61DfVGJO/7F92UIwf7wYxrVrVdvuAAa7ZfrfdchJWUQgnn7W1rg+uN4gpUfP9r371K8D6lBqPNd8nVVnu3pflaxro3se+uowJIvuXHRRPPAEVFW5k/CabwODB7r6qyvqBZlo4KV2zxiWlPiqlZ555ZhYCMwXDktKk+pa792jZmnVs3MdmrTAmiCwpDQJVmDXL9R39859zHU3xiayUgq9K6ZFHHpnhoExBsaQ0qchKqTEmmDrlOgCTBosWwZIlsPPOuY6kOJWXu/twUuqjUlpdXU11dXWGAzMFw/qUJlXlVUqr19i0UMYElVVKg2D2bHdvSWluRFdKfSSlv/71rwHrU2o8VilNqrVSakmpMUFlSWkQzJrl+o0OGZL8WJN+7Wi+/7N1szCRLClNKlTSmYrSEqqt+d6YwLKkNAhmz4attkpYmTMZFDnQCXxVSg899NAMB2UKijXf+9K3PMQyq5QaE1jWpzQIZs2ypvtcakeldPHixSxevDjDgZmCYZVSXyrLu1rzvTEBZpXSQrdkCSxYALvskutIilc7+pQeddRRgPUpNR5LSn2pKg/x+dI1uQ7DGJMhlpQWune9pWqtUpo77Rh9f8EFF2Q4KFNQLCn1pbK8K2/Ns0qpMUFlSWmhmzXL3dsgp9wJN9NHNt936pQwuRg+fHgWAjMFo7S09bH1KY2rqjxETV0jjc0tdOlsvc+MCRr7V13oZs+GzTcHW0M9dzp1gm7d2lZKy8oSrqT17bff8u2332YpQJP3rFLqS6U3V2lNrY3ANyaIrFJa6GbPhqFDcx2FKStrHX1fV5dwkBPAscceC1ifUuOxpNSXvt5cpcvWrGODHqVJjjbGFBpLSgvZ8uXw1Vdw2mm5jsSUla1fKU3gkksuyUJQpmBYUupLuFJqS40aE0yWlBay8CAnG3mfeykmpfvtt18WgjIFw+Yp9cWWGjUm2KxPaSELLy+60065jcO4EfiRA52SNN/PmzePefPmZSEwUxDCSWlJScK+yMWudalRq5QaE0RWKS1ks2bB4MHQp0+uIzEpVkpPPPFEwPqUGk84GbWm+4QqQiV0LelklVJjAsqS0kI2e7bNT5ovysrcIgbgKqW9eyc8/Morr8xCUKZgiLhqqTXdJyQiVJV1pdoqpcYEkiWlhWrlSvjiCzjhhFxHYqDt6HsfldK99947C0GZghIKWaXUh6qKkFVKjQko61NaqGwlp/ySYvP9p59+yqeffpqFwEzBsKTUl6ryEMtrLSk1JoisUlqowoOcLCnND5FJqY+BTn/4wx8A61NqIoRCrm+pSaiyrCsfLVyV6zCMMRlgfwEL1axZMHAgbLBBriMx0Hb0vY9K6bXXXpuFoExBKS21kfc+VFW4SqmqIvZ+GRMolpTmM1U44wxXPRkzpu362DbIKb+UlUFTE6xdCw0NSSule+yxR5YCMwUjcq5SE1dlWVcam5VVa5vo2d0GhhkTJNanNJ/ddRfccQfccgvstRd8843bvmYNfPqpJaX5JFwZXbq07fM45s6dy9y5czMclCko1qfUl74VLnlfZoOdjAkcS0rz1ccfwznnwIEHwtNPw2efuST0pZdgzhxXRbWVnPJHiknp6aefzumnn57hoExBsaTUl8qy8FKjlpQaEzTWfJ+P1q2D//s/qKiA8eOhf3/Ydls48kiXpA4d6o6zSmn+iE5KkzTf33DDDRkOyBScfv1cFxCTUFWFS9xtrlJjgseS0nx00UXw3nswebJLSAG22AJmzICTT4aJE90X2IYb5jZO0yqclC5b1vZ5HLvttluGAzIF5/77XQuISeiHSqlNC2VM4FhSmm9efBFuvhlGj4Zf/rLtvrIyeOwx2H9/G6mbb8rL3b3PSumcOXMAGDJkSCajMoUkySpgxulT1hURqF5tSakxQWNJaT6probjj4dttoEbb4x9jAh466abPJJin9KzzjoLsHlKjUlV505Cn+5dqa615ntjgsaS0nyxdCkceywsXw4vvADduuU6IpOKFJPSsWPHZjggY4KrqjxklVJjAsiS0lxbtw5uuw2uvtqtBHTHHbDjjrmOyqQqxYFO1mxvTPtVlndluVVKjQkcmxIqV1Thn/90o+rPOw9+/nOYOxdOOSXXkZn2SHGg0zvvvMM777yT4aCMCaaq8hDVNiWUMYFjldJceO89OPtsePVVl5ROnQoHHJDrqExHpDjQ6bzzzgOsT6kx7VFZ3pXlNiWUMYFjSWk2LVkCl14K990Hffq4pvpTTnHLiJrCFu4D7LNP6e23357hgIwJrqryEGvWNVHf2Expl865DscYkyaWDWXDunVuqdBrrnFro599tktOe/XKdWQmXTp1ctXRujo3Q0JpacLDt9tuuywFZkzw9C13c5VWr1nHwN6JWyWMMYXDktJMmzMHRoyAefPg0EPdVE9bbJHrqEwmlJW5pLR796RzyL7xxhsA7LHHHtmIzJhAqSxvXdXJklJjgsOS0kzbZBPYeGO4807rNxp0ZWVuoFOSpnuAiy66CLA+pca0R1W4UmrTQhkTKJaUZlrv3mCJR3EIJ6NJBjkB3H333RkOxpjgCldKbalRY4LFklJj0iU8At9HpXTLLbfMcDDGBNcPlVIbgW9MoNg8pcakSzgZ9ZGUvvbaa7z22msZDsiYYCrt0pnyUInNVWpMwFil1Jh0SaH5/vLLLwesT6kx7VVV3tUqpcYEjCWlxqRLCpXSBx54IMPBGBNsleUhllul1JhAsaTUmHRJoVK62WabZTgYY4Ktqrwr86trcx2GMSaNrE+pMemSQqX0pZde4qWXXspwQMYEl6uUWvO9MUFilVJj0iWF0ffXXHMNAPvtt18mIzImsKrKQ3xf10BTcwslna2+YkwQWFJqTLqk0Hz/yCOPZDgYY4KtqrwrqlBT10jfilCuwzHGpIElpcakSwrN9xtvvHGGgzEm2FrnKl1nSakxAZGTNg8RGS0i80WkXkRmicheCY4dJiIa47ZV1HEjROQjEVnn3R+R+Z/EmAgpVEqnTJnClClTMhyQMcEVTkqtX6kxwZH1pFRERgK3ANcCOwFvAC+IyKAkp24LbBhx+zzimrsDk4BHgSHe/RMiMjTtP4Ax8aRQKR0zZgxjxozJcEDGBFd4qVGbQN+Y2FIpAHrH7+0dVy8i80Tk1GzFGpaL5vtzgPGqeq/3/AwRGQ6cBlyY4LylqlodZ99ZwKuq+lfv+V9FZB9v+/+lI2hjkkohKZ04cWKGgzEm2CKb740xbUUUAEcD//PuXxCRbVT1mxjHbwo8DzwAHAPsCYwTkWWq+lS24s5qUioiXYFdgBujdk0D9khy+kwRCQEfAdeo6qsR+3YHbos6fipwegfCNSY14dH3Pprv+/fvn+FgjAm2HqUldO3cibkLVvLWvOW5DscYAPr1KGXTquSFiSxItQB4KrBQVc/wnn/stTafCwQzKQWqgM7AkqjtS4B4c+Mswr2J7wBdgWOBl0Vkb1X9r3dM/zjXtG9+kz0bbdT2PoHJkycDcOihh2YyImMCS0QY0Lsb/5yzkH/OWZjrcIwB4JifDuKaw7fPaQztLADu7u2PNBU4XkS6qGpjeqOMLe9H36vqp8CnEZveFJHBwHnAf2Odk4yInAKcAjBgwABbf9ykTemjj1Lf2AhJfqcuvfRSACoqKrIQlTHBdNFOSkOTjbw3+aOk87Js5BQlIjIz4vk9qnpPxPP2FAD7A9EruizB5YlVuAJhxmU7Ka0GmoF+Udv7AYtTuM4M4KiI54tTuab34d0DUFZWpsOGDUvhpY3puPBqTlVVVTmOxBhjTIFpUtVdcx1EJmR19L2qNgCzgP2jdu2PG4Xv1xDaZu1vpuGaxmRNVVWVJaTGGGMyoT0FwHjFvSbvelmRi+b7m4FHRORt4HVc59qNgLsARORhAFU9znt+FvAV8CGuT+kxwOHAiIhr3gL8R0QuAP4JHAHsgxs9ZkzeefrppwE48sgjcxyJMcaYIFHVBhEJFwCfiNi1P/EHLb2Jy50i7Q/MzFZ/UshBUqqqk0SkErgEN9/oXOBgVf3aOyR6vtKuwA3AQGAtLjk9RFWfj7jmGyJyFHANcBXwJTBSVWdk9Icxpp1uvfVWwJJSY4wxGZFSAdDbfrqIjAXuBn4GjCLL02qKqmbz9fJOWVmZ1tbW5joMU2RWrlwJQM+ePXMciTHGmEIiInWqmnTeKREZDfyF1gLg2ar6H2/fdABVHRZx/N7A33GLFS0ErlfVu9Idf8KYLSm1pNQYY4wxhcFvUlqIsr7MqDEGJk2axKRJk3IdhjHGGJM3rFJqlVKTA+FpyGyOXGOMMakIcqXUklJLSk0O1NXVAdDdx5KkxhhjTFiQk9K8X9HJmCCyZNQYY4xpy/qUGpMDEyZMYMKAqKr3AAAPEElEQVSECbkOwxhjjMkb1nxvzfcmB6xPqTHGmPYIcvO9JaWWlJocaGx0C2R06dIlx5EYY4wpJEFOSq1PqTE5YMmoMcYY05b1KTUmB8aPH8/48eNzHYYxxhiTN6z53prvTQ5Yn1JjjDHtEeTm+6JPSkWkBVibxkuWAE1pvJ5JL/t88pd9NvnNPp/8ZZ9Nfkv359NNVQPZ0l30SWm6ichMVd0113GY2OzzyV/22eQ3+3zyl302+c0+H/8CmWkbY4wxxpjCYkmpMcYYY4zJOUtK0++eXAdgErLPJ3/ZZ5Pf7PPJX/bZ5Df7fHyyPqXGGGOMMSbnrFJqjDHGGGNyzpJSY4wxxhiTc5aUpkhERovIfBGpF5FZIrJXkuP39o6rF5F5InJqtmItRql8PiJypIhME5FlIrJaRGaIyGHZjLeYpPpvJ+K8PUWkSUTmZjrGYtaOv21dReQq75x1IvKNiJyZrXiLSTs+m6NFZI6I1InIYhGZICL9sxVvMRGRn4vIv0RkgYioiIzycc72IvKaiKz1zrtMRCQL4eY9S0pTICIjgVuAa4GdgDeAF0RkUJzjNwWe947bCbgOuE1ERmQn4uKS6ucD7A28AhziHf888IzfZMn4147PJnxeb+Bh4OWMB1nE2vn5TASGA6cAWwK/Ad7PcKhFpx3fOz8DHgEeArYFDge2AR7NSsDFpxyYC/wJHwvxiEgP4EVgCbCbd955wDkZjLFg2ECnFIjIDOB9Vf19xLbPgSdV9cIYx18PHKmqm0dsuw/YVlV3z0bMxSTVzyfONd4G/quqf85QmEWpvZ+NiDwNvAcI8GtV3S7jwRahdvxtOwB4AviRqlZnL9Li047P5lzgDFXdJGLbCcBtqlqejZiLlYisAU5X1fEJjjkNuB7op6prvW2XAKcBA7XIkzKrlPokIl2BXYBpUbumAXvEOW33GMdPBXYVkS7pjbC4tfPziaUCqElXXKb9n42IjAb6AddkLjrTzs/ncOAd4BwR+U5EPheRW0XEkp40audn8zqwoYgcKk4VcBSuJcjk3u64wkdkVXUqsBEwOCcR5RFLSv2rAjrjSu6RlgDx+ur0j3N8iXc9kz7t+XzaEJE/AgNxTV8mfVL+bERke+By4BhVbc5seEWvPf92NgP2BHYERgCn45ryx2cmxKKV8mejqm/iktBHgQZgGa6l4fjMhWlSEC8vCO8rapaUGgN4/XxvAI5W1a9zHU8xE5EQMAk4V1Xn5zoeE1MnQHH/Xmao6lRcYjpCRPrlNrTiJiLbALcBV+OqrMNxyc7duYzLGD9Kch1AAakGmnHNiZH6AYvjnLM4zvFN3vVM+rTn8wFARH6NG0xznKpOzkx4RS3Vz2ZDYGvgQRF50NvWCRARaQIOVtXo5kzTfu35t7MIWKCqKyO2fezdD2L9SpBpn/Z8NhcCb6vqDd7z90WkFviviFykqt9lJlTjU7y8ILyvqFml1CdVbQBmAftH7dofNxoyljfjHD9TVRvTG2Fxa+fng4j8FtdcP0pVn8xchMWrHZ/NAmB7YEjE7S7gC+9x3M/TpK6d/3ZeBzaK6kO6hXdvLQ1p0s7PpjsukY0Ufm7f+bn3JrCXiJRGbNsfWAh8lZOI8omq2s3nDRiJ66NzMq6ScwuwBtjE2/8w8HDE8ZsCtcBY7/iTvfNH5PpnCeKtHZ/PUUAjbkqO/hG3Prn+WYJ2S/WziXH+FcDcXP8cQb21499OOfAtbgT+tsDPcNPiPJHrnyVot3Z8NqO8v2un4fr+/gw3KG1Wrn+WIN68fwvh/zzXAZd5jwd5+68DXo44vieuIjoR2A44ElgF/DnXP0s+3Kz5PgWqOklEKoFLcE2Mc3FNieHKwKCo4+eLyMHA33F/IBYCZ6rqU1kMu2ik+vkAp+K6sIz1bmGvAcMyG21xacdnY7KoHX/b1ojIfri+i+/gZqz4J3BB9qIuDu34bMaLSAWuj+9NwErcfMznZy/qorIr8GrE8yu920O4/yBsCPwovFNVV4rI/sAdwEzcv52bgJuzFG9es3lKjTHGGGNMzln/EmOMMcYYk3OWlBpjjDHGmJyzpNQYY4wxxuScJaXGGGOMMSbnLCk1xhhjjDE5Z0mpMcYYY4zJOUtKjclzIrK5iNwuIh+LyBoRWS0in4jIvSLy04jjvhIRFZGvchhuOJbxXiwqIoMjtvcTkUdFZJGINHv7x4rI4Ijjx2cwrl4icoV3O9xv3NkiIsMiXj/Z7QrvnPDz6dmON5lMfq6pfFZR72ta4zDGpI9Nnm9MHhORE4A7gVDUri29W19gveQqj92CW6EmV3oBl3uPH8JN+G6MMSYPWFJqTJ4SkX2B+3AtGgr8FbgbWApsAvya1vXG84qqjsKtZhJtF+9+BbCpqq6I2CcZDiupBHFn6/WnE/E+iMgo4EHv6UNefGknIqWqWp+JaxtjjF/WfG9M/rqO1n+jt6rqpar6nao2qOrnqnod8PtEFxCRISLytIh8ISKrRKRRRBZ723aNOnZTEXlYRL4RkXoRWSEic71m0g0ijvu9iMwUke9FZJ2ILBCRF0Xk+Ihj2jSthptPgR97h/QCarz9oxI184rIziLyD+91GkSkWkReFZGfePvLReQhEflARJZ7P+MKEfmPiIyMuM4VwPyISx8f/ZoJuh2UiciVIvKhiKwVkToReVdEzhGRkojj2vwcInKc9x6uFdf94ngySET2FZG3vNf7UkT+IiKRSe4VEfEdISL3i0g1sDbimK1F5JGI93upiDwpIjtEvZav35eoc34rIu8nej9EZC8R+ZeILIv4fZ0Y/foJ3oONvHjXeL8PdwIVcY5N+WcwxmSQqtrNbnbLsxuwAa46Gr4N8HHOV96xX0VsOyrqOpG3WmDriGM/THDsdt4xv0lwzJMR1xofsX0wMCzBeaO8Y8LPx0dc5wigMd553jH9E1xbgeO8465IcMz4WHF728qAWQnOfR7o5B0b+XPUxDl+zxR+D0bFel+ijgnvr47zXh0TcewVUcf/cJy3f0+gLk7ca4G9Uvx9iXw/Fid7P4BjgOY4x9UDw+L9jnnbugEfxzh3Yaz30c/PYDe72S17N6uUGpOfBkc8XqWqC9p5ndnAgcCGuH6pPYDTvH3dgT8AiEglsI23/VZcItYH2A24FFjp7fu5d78G16c1hOtK8FtgSrwgVHW6qgrwtbfpa1UV7zY+1jki0g24l9ZuRpcB/YAqXHI8z9u+GtdPdbD3M5UCe+CSK4BzvBiuADaNeImHImIYFS924CxgZ+/xVNx7uRnuvQU4CJf8R+sFjAZ6AtdHbD82wWt1RCXwN6A3cLqP1xNgOO49297bdi8usfsa19UiBOwELMO9r3dASr8vkfqR4P0QkTLgNlzrQBPuPyQ9gFO940K47iuJHAds5T1+CxiIq86viD6wnT+DMSaDrE+pMcG2GDgJGItL2rpF7d/Su6/BfXH3wiVZq3EVp/dU9ZqI4+d792XAJbgK4sfANFVN95f4z3CJFsB0Vb06Yt+TEY/rcInqJGBrXFNtZP/ULemYQyIeX6iqiwFE5CpaB0odDDwWdd4sVb3TO3YCcL63fZMOxhPPEuAyVW0WkYeA25O83k2qOtV7PFdENqc1odsE99lG215E+uP6Nfv5fYmU7P34mXc9gOdVNfze3i0ipwJDgC1E5Meq+kWc19g34vF14f/MichNuP7Zkfz+zhtjssQqpcbkp68iHvcQkY3aeZ3Hgb/gkrXohJTwNlVtwVWsvgM2By4GJgAfeH01N/aOHwc8AYSPH4urHi4RkQvaGWM8/SIef5TguPNxFbyhuMpa9ICp0g7G0Tfi8TcRj7+OeByr/+GnEY9r0xhPPF+qanMKr/du1HO/fSgrU/h9iZTs/Yj3PkPy9/qH2CIefxfnMZDS77wxJkssKTUmD6nqUuDtiE3nxToucpBNjH29cU334Kpo2wKdgZgDRlT1OWAQrrJ4GHAVrn/fdriqKKpar6q/xTVz7gmcCMzANa1eKyID/P2EviyJeLx1guMim84PB0JeV4HlMY7VdsSxNOLxoDiPI48Ja+zg66bqh9dTVT+vtzbqeeTP8FJE14Yfbri+sx96r5H09yVefMR+P+K9z9HPY73XYdURjwfGedwaROo/gzEmgywpNSZ/XYyrSAKc6Y2c3khEuoibUP8iXB/AeJpo/fJv4v/bu3vQKKIoiuP/Wwi2FlYBEcTGxk6wEo2NTRBsFWwMCCqCjQRUVCKxkhCwEG2CQhA0CIqCBo2opSSpjKYT0UqDSVhiVp7FeUtmYXez+WISOT8IzLJvdnfChJx9c+8b+I0uc19vNDgiBoBOVC/6AngEzOend+QxxyLiDNABjKNZ0/HaS9Dkn/8KvWcxWB6MiJ6I2B4R2yLiaETU6lurhX2mgS0RcYn6WbOaYlDdnesYl/KssN0bugHATlTj2mjMppRS+gJ8zg87I+J86GYDWyNib0RcBoZq49s5X5bpA7qkDnAkIrpCKyucQnWtAJMtLt0DvC5sX4yIjojYBVxoNHgdjsHMVsGh1GyDSim9Qo1IC+hv9QrwDfiDwkMvampptv8MMJIfdgBf0ezjnia7nAZeFt5jHDXBgC7Rg2YsB9Dl9Jn8052f+w5MLOMQW0opVdCSV7XQ2YtmyX4Cw6jZiLxd8wYFjHM0aG5JKc2ijmtQM9RsXh7pZIuP0k99U9MPVFtbW3P1Oapn/R90oy73AG6hkFgBxoCr1JdUtHO+tC2lNAecRV/EtgBP0Pl1Jw+ZZ7HpqZlB4FPe3o8uzU9RXxpQtKbHYGar41BqtoGllO6iy+23URCtoHq8SeAe0LfESxxHgekX6ia+T/M7KvUB71Dwq6IGoo8o4PXnMSOooWcKhb+/KIwOAQdykFwzKaVhVCs6hJb1qaJQOspinelN4AYKFpX83CGad0+fAN6imeN2PsMcWnXgGmqEmUfBbQzNwHXl+sRNL6U0isL2IAp0C+j3PYG+jPQUhrdzviz3/R+g5cOeolntKvoi9RDYl3RzgVb7V4DDwGP0dzKNbj7QbD3fNT8GM1u5aK/0yMzMzMxs/Xim1MzMzMxK51BqZmZmZqVzKDUzMzOz0jmUmpmZmVnpHErNzMzMrHQOpWZmZmZWOodSMzMzMyudQ6mZmZmZlc6h1MzMzMxK9w9SV9cJoDUPPgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(figsize=(10,7))\n",
    "ax1.plot(class_thresh_arr, bal_acc_arr_transf)\n",
    "ax1.set_xlabel('Classification Thresholds', fontsize=16, fontweight='bold')\n",
    "ax1.set_ylabel('Balanced Accuracy', color='b', fontsize=16, fontweight='bold')\n",
    "ax1.xaxis.set_tick_params(labelsize=14)\n",
    "ax1.yaxis.set_tick_params(labelsize=14)\n",
    "\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(class_thresh_arr, np.abs(1.0-np.array(disp_imp_arr_transf)), color='r')\n",
    "ax2.set_ylabel('abs(1-disparate impact)', color='r', fontsize=16, fontweight='bold')\n",
    "ax2.axvline(best_class_thresh, color='k', linestyle=':')\n",
    "ax2.yaxis.set_tick_params(labelsize=14)\n",
    "ax2.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```abs(1-disparate impact)``` must be small (close to 0) for classifier predictions to be fair.\n",
    "\n",
    "For a classifier trained with reweighted training data, at the best classification rate, this is indeed the case.\n",
    "This implies fairness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(figsize=(10,7))\n",
    "ax1.plot(class_thresh_arr, bal_acc_arr_transf)\n",
    "ax1.set_xlabel('Classification Thresholds', fontsize=16, fontweight='bold')\n",
    "ax1.set_ylabel('Balanced Accuracy', color='b', fontsize=16, fontweight='bold')\n",
    "ax1.xaxis.set_tick_params(labelsize=14)\n",
    "ax1.yaxis.set_tick_params(labelsize=14)\n",
    "\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(class_thresh_arr, avg_odds_diff_arr_transf, color='r')\n",
    "ax2.set_ylabel('avg. odds diff.', color='r', fontsize=16, fontweight='bold')\n",
    "ax2.axvline(best_class_thresh, color='k', linestyle=':')\n",
    "ax2.yaxis.set_tick_params(labelsize=14)\n",
    "ax2.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```average odds difference = 0.5((FPR_unpriv-FPR_priv)+(TPR_unpriv-TPR_priv))``` must be close to zero for the classifier to be fair.\n",
    "\n",
    "For a classifier trained with reweighted training data, at the best classification rate, this is indeed the case.\n",
    "This implies fairness."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Summary of Results\n",
    "We show the optimal classification thresholds, and the fairness and accuracy metrics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification Thresholds\n",
    "\n",
    "| Dataset |Classification threshold|\n",
    "|-|-|\n",
    "|Adult||0.2674|\n",
    "|German|0.6732|\n",
    "|Compas|0.5148|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fairness Metric: Disparate impact, Accuracy Metric: Balanced accuracy\n",
    "\n",
    "#### Performance\n",
    "\n",
    "| Dataset |Sex (Acc-Bef)|Sex (Acc-Aft)|Sex (Fair-Bef)|Sex (Fair-Aft)|Race/Age (Acc-Bef)|Race/Age (Acc-Aft)|Race/Age (Fair-Bef)|Race/Age (Fair-Aft)|\n",
    "|-|-|-|-|-|-|-|-|-|\n",
    "|Adult (Test)|0.7417|0.7128|0.2774|0.7625|0.7417|0.7443|0.4423|0.7430|\n",
    "|German (Test)|0.6524|0.6460|0.9948|1.0852|0.6524|0.6460|0.3824|0.5735|\n",
    "|Compas (Test)|0.6774|0.6562|0.6631|0.8342|0.6774|0.6342|0.6600|1.1062|\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fairness Metric: Average odds difference, Accuracy Metric: Balanced accuracy\n",
    "\n",
    "#### Performance\n",
    "\n",
    "| Dataset |Sex (Acc-Bef)|Sex (Acc-Aft)|Sex (Fair-Bef)|Sex (Fair-Aft)|Race/Age (Acc-Bef)|Race/Age (Acc-Aft)|Race/Age (Fair-Bef)|Race/Age (Fair-Aft)|\n",
    "|-|-|-|-|-|-|-|-|-|\n",
    "|Adult (Test)|0.7417|0.7128|-0.3281|-0.0266|0.7417|0.7443|-0.1991|-0.0395|\n",
    "|German (Test)|0.6524|0.6460|0.0071|0.0550|0.6524|0.6460|-0.3278|-0.1944|\n",
    "|Compas (Test)|0.6774|0.6562|-0.2439|-0.0946|0.6774|0.6342|-0.1927|0.1042|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
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  "language_info": {
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    "name": "ipython",
    "version": 2
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
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